Criminal Deterrence: A Review of the Literature

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Criminal Deterrence: A Review of the Literature Criminal Deterrence: A Review of the Literature

Aaron Chal9n

University of Pennsylvania

Justin McCrary

Columbia Law School

, jmccrary@law.columbia.edu

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Aaron Chal9n & Justin McCrary,

Criminal Deterrence: A Review of the Literature

, 55 J. ECON. LIT. 5 (2017).

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Journal of Economic Literature 2017, 55(1), 5–48

https://doi.org/10.1257/jel.20141147

1. Introduction

he day-to-day work of individuals

employed in law enforcement, correc-

tions, and other parts of the criminal-jus-

tice system involves identifying, capturing,

prosecuting, sentencing, and incarcerating

offenders. Perhaps the central function of

these activities, however, is deterring indi-

viduals from participating in illegal activity

in the rst place. Deterrence is important

not only because it results in lower crime

but also because, relative to incapacitation,

it is cheap. Offenders who are deterred from

committing crime in the rst place do not

have to be identied, captured, prosecuted,

sentenced, or incarcerated. For this rea-

son, assessing the degree to which poten-

tial offenders are deterred by either carrots

(better employment opportunities) or sticks

(more intensive policing or harsher sanc-

tions) is a rst-order policy issue.

The standard economic model of criminal

behavior draws on a simple expected utility

model introduced in a seminal contribution

by the late Gary Becker. This model envi-

sions crime as a gamble undertaken by a

rational individual. According to this frame-

work, the aggregate supply of offenses will

depend on social investments in police and

prisons as well as on labor-market opportu-

nities that increase the relative cost of time

spent in illegal activities.

Using Becker’s work as a guide, a large

empirical literature has developed to test

the degree to which potential offenders are

deterred. The papers in this literature fall

into three general categories. First, a num-

ber of papers consider the responsiveness

of crime to the probability that an individ-

ual is apprehended. This concept has typi-

cally been operationalized as the study of the

sensitivity of crime to police, in particular

Criminal Deterrence:

A Review of the Literature

†

A C  J MC

We review economics research regarding the effect of police, punishments, and work

on crime, with a particular focus on papers from the last twenty years. Evidence in

favor of deterrence effects is mixed. While there is considerable evidence that crime is

responsive to police and to the existence of attractive legitimate labor-market oppor-

tunities, there is far less evidence that crime responds to the severity of criminal sanc-

tions. We discuss fruitful directions for future work and implications for public policy.

( JEL J64, K42)

Chaln: University of Pennsylvania. McCrary: Univer-

sity of California, Berkeley and NBER.

†

Go to https://doi.org/10.1257/jel.20141147 to visit the

article page and view author disclosure statement(s).

Journal of Economic Literature, Vol. LV (March 2017)

police manpower or policing intensity. A sec-

ond group of papers studies the sensitivity of

crime to changes in the severity of criminal

sanctions. This literature assesses the respon-

siveness of crime to sentence enhancements,

three strikes laws, capital-punishment

regimes, and policy-induced discontinuities

in the severity of sanctions faced by partic-

ular individuals. The third group of papers

examines the responsiveness of crime to

local labor-market conditions, generationally

operationalized using either the unemploy-

ment rate or a relevant market wage. This

literature seeks to determine whether crime

can be deterred through the use of positive

incentives rather than punishments.

The papers in each of these literatures

can be viewed as measuring the degree to

which individuals can be deterred from par-

ticipation in criminal activity. Each of the

literatures is vast and it is not unreasonable

to suggest that each could merit a separate

review. A challenge remains to characterize

the pattern of the empirical ndings and

explain why individuals appear to be more

responsive (and thus more deterrable) along

certain margins than along others. In this

article, we provide a brief review of each of

the three literatures introduced above with

the intention of rationalizing several appar-

ently divergent ndings.

We are not the rst to review the deter-

rence literature. Indeed, in last decade we

count a number of comprehensive reviews

on the subject including, but not limited

to, Levitt and Miles (2006), Tonry (2008),

Durlauf and Nagin (2011), Nagin (2013),

and Chaln and Tahamont (forthcoming).

We attempt to differentiate our review in

several ways. First, we have tried to synthe-

size research carried out by economists, as

well as criminologists. In this goal, we are

not alone. However, we are hopeful that by

highlighting in the JEL research from crim-

inology that is typically unknown to econo-

mists, we will help to further integrate the

two disciplines. Second, interest in deter-

rence research has multiplied rapidly over

the last few years, with a number of import-

ant studies having been published in the last

year or two alone. Accordingly, we have done

our best to include references to the new-

est and most cutting-edge research. Finally,

in this review, we cover a topic that is often

omitted from reviews of the deterrence liter-

ature—the role of labor markets in deterring

crime via “carrots” rather than “sticks.” The

remainder of the paper is laid out as follows:

section 2 considers research on the effect

of police on crime, section 3 considers the

effect of prison and/or sanctions on crime,

and Section 4 considers the responsiveness

of crime to local labor-market conditions.

Section 5 concludes.

2. Theories of Deterrence

Deterrence is an old idea and has been

discussed in academic writing at least as far

back as eighteenth-century treatises by Adam

Smith (1776), Jeremy Bentham (1789), and

Cesare Beccaria (1764). There are three core

concepts embedded in theories of deter-

rence—that individuals respond to changes in

the certainty, severity, and celerity (or imme-

diacy) of punishment. Interestingly, in the

criminological tradition, deterrence is often

characterized as being either general or spe-

cic, with general deterrence referring to the

idea that individuals respond to the threat of

punishment and specic deterrence referring

to the idea that individuals are responsive to the

actual experience of punishment. Economics

prefers different terminology, reserving the

term deterrence for what the criminologist

calls general deterrence and describing spe-

cic deterrence as a change in information or,

perhaps more exotically, a change in prefer-

ences themselves. In this section, we briey

characterize the way economists have formal-

ized these concepts. In general, economic

theories of deterrence have focused more

Chaln and McCrary: Criminal Deterrence: A Review of the Literature

heavily on certainty and severity. However,

recent writing has increasingly characterized

deterrence as part of a dynamic framework in

which offender behavior is sensitive to their

time preferences (Polinsky and Shavell 1999

and Lee and McCrary forthcoming).

2.1 Economic Models of Crime

The earliest formal model of criminal

offending in economics can be found in

Becker’s seminal 1968 paper, “Crime and

Punishment: An Economic Approach.” The

crux of Becker’s model is the idea that a ratio-

nal offender faces a gamble. He can either

choose to commit a crime and thus receive a

criminal benet (albeit with an associated risk

of apprehension and subsequent punishment)

or not to commit a crime (which yields no

criminal benet but is risk free). The expected

cost of committing a crime is a function of

the offender’s probability of apprehension, p ,

and the severity of the sanction that he will

face upon apprehension, f . To be more spe-

cic, the individual can be said to face three

potential outcomes, each of which delivers a

different level of utility: (1) the utility associ-

ated with the choice to abstain from crime,

; (2) the utility associated with choosing

to commit a crime that does not result in an

apprehension, U

; and (3) the utility associ-

ated with choosing to commit a crime that

results in apprehension and punishment,

. In such a formulation, the individual

chooses to commit a crime if, and only if, the

following condition holds:

(1) (1 − p) U

+ p U

> U

That is, crime is worthwhile so long as its

expected utility exceeds the utility from

abstention.

The “if and only if ” holds if we maintain that the case of

(1 − p) U

+ p U

= U

implies no crime, an unimportant

assumption we make henceforth to simplify discussion.

In addition to the clear role played in this

model by the probability of apprehension, p ,

the formulation also suggests the importance

of two additional exogenous factors that

could inuence U

and U

. Crime becomes

more attractive when the disutility of appre-

hension is slight (e.g., less unpleasant prison

conditions) and it becomes less attractive

when the utility of work is high (e.g., a low

unemployment rate or a high wage). Becker

operationalizes the disutility associated with

capture using a single exogenous variable,

f , which he refers to as the severity of the

criminal sanction upon capture. Typically, f

is assumed to refer to something like a ne,

the probability of conviction, or the length of

a prison sentence.

To a large degree, then,

government maintains control over U

The utility associated with abstaining

from crime, U

, is principally a function of

the individual’s ability to derive utility from

non-illicit activities. In practice, this is typ-

ically thought of as the wage that can be

earned in the legal labor market. When the

legal wage rises, U

rises, thus reducing the

relative benet of criminal activity. It is fair

to say that while government maintains some

control over U

, it does so to a lesser extent

than it does over the utility of punishment,

Using these ideas, Becker rewrites (see

footnote 16) the expected utility confronting

an individual contemplating crime as

(2) EU = pU(Y − f ) + (1 − p) U(Y) ,

where Y represents the income associ-

ated with getting away with crime.

In this

In principle, f can be a function of many different

characteristics of the sanction including the length of the

sentence, the conditions under which the sentence will be

served, and the degree of social stigma that is attached to a

term of incarceration, all of which are likely heterogeneous

among the population.

As Becker is careful to say, income “monetary and

psychic.”

Journal of Economic Literature, Vol. LV (March 2017)

formulation, crime occurs if and only if

EU > U

. Equivalently, we can dene

an indifference point, Y

∗

, such that crime

occurs if and only if Y > Y

∗

. It is easy to see

that

(3)

U( Y

∗

) − U

____________

|U( Y

∗

− f ) − U

___

1 − p

Several important ideas are embedded

in (3). First, for the individual to elect to

engage in crime, the gain relative to its loss

must exceed the odds of capture. Dividing

the numerator and denominator of the left

side by U

yields a natural interpretation,

in terms of percentages. Consider a crim-

inal opportunity where capture is n times

as likely as not. Crime occurs if the antici-

pated percent improvement in utility associ-

ated with getting away with it is more than

n times as large as the anticipated percent

reduction in utility associated with appre-

hension. Second, an increase in p unambigu-

ously reduces the likelihood of crime, as this

increases the right-hand side of (3). Third,

an increase in f unambiguously reduces the

likelihood of crime as long as U′( ⋅ ) > 0 , as

this decreases the left-hand side of (3).

Under risk neutrality, the equation (3)

simplies. Dene a as the income associated

with abstaining from crime, i.e., U(a) = U

;

dene c = f − b > 0 as the effective cost

of punishment. Further dene income,

Y = a + b and Y

∗

= a + b

∗

, where b is the

criminal benet and b

∗

is the criminal ben-

et at which the individual is indifferent

between crime and abstention. Then equa-

tion (3) reduces to

∗

= c

___

1 − p

This simplied version of the Becker model

is the starting point of the dynamic analysis

in Lee and McCrary (forthcoming).

A somewhat different focus can be found

in Ehrlich (1973), where the notion of the

opportunity cost of engaging in crime is

front and center. Perhaps unsurprisingly,

labor economists have found it particularly

attractive to view crime as a time-alloca-

tion choice, and this type of formulation is

found in several prominent papers includ-

ing Lemieux, Fortin, and Frechette (1994);

Grogger (1998); Williams and Sickles (2002);

and Burdett, Lagos, and Wright (2004),

among others.

The typical time-allocation model of crime

considers a consumer facing a constant mar-

ket wage and diminishing marginal returns

to participation in crime. This consumer

maximizes a utility function that increases in

both leisure ( L ) and consumption ( C ), where

consumption is nanced by time spent

engaged in either legitimate employment

( h

) at a market wage ( w ) or time spent

in crime ( h

) with a net hourly payoff of r .

The consumer’s constrained optimization

problem is to maximize his utility function,

U(C, L) , subject to the consumption and

time constraints:

(4) C = w h

+ r h

+ I

(5) L = T − h

− h

In (4), consumption is shown to be equal

to an offender’s legitimate income plus his

non-legitimate income.

In (5), T is the indi-

vidual’s time endowment and leisure is the

remaining time after market work and time

spent in crime is accounted for.

The param-

eter r reects the criminal benet but it

For further details, see Gronau (1980).

I represents nonlabor income.

Grogger assumes that the returns to crime dimin-

ish as the amount of time devoted to criminal activity

increases—i.e., there is a concave function r ( ⋅ ) that

translates hours spent participating in crime into income.

Diminishing returns implies that those engaging in crimi-

nal activity rst commit crimes with the highest expected

payoffs (lowest probability of getting caught and high-

est stakes) before exploring less lucrative opportunities.

However, this need not be true.

Chaln and McCrary: Criminal Deterrence: A Review of the Literature

also reects the costs of committing crime,

namely the risk of capture and the expected

criminal sanction if captured. In other words,

r can be thought of as the wage rate of crime,

net of the expected costs associated with the

criminal-justice system. In this way, criminal

sanctions drive a wedge between the con-

sumer’s productivity in offending and his

market wage, in turn, incentivizing market

work over crime.

An interesting question in both Becker’s

model and Ehrlich’s model is whether indi-

viduals are more deterred by increases in p

or f . Becker addresses this in a straightfor-

ward way by asking whether the expected

utility of crime is decreased more by a small

percent increase in p or an equivalent per-

cent increase in f . This makes sense because

participation in crime should be monotonic

in its expected utility. Becker’s analysis

shows that p is more effective if and only if

U″( ⋅ ) > 0 , i.e., if and only if individuals were

risk preferring.

If individuals are averse

to risk, increasing f is more effective than

increasing p , and if individuals are risk neu-

tral, then f and p are equally effective. Becker

notes (footnote 12) that this conclusion is the

opposite of that given by Beccaria regarding

the effectiveness of punishment versus cap-

ture, and that the conclusion is similarly at

odds with contemporaneous views of judges.

In order for an individual to commit any crime at all,

there are two necessary and sufcient conditions. First,

the marginal return to the rst instant of time supplied to

crime must exceed the individual’s valuation of time (in

terms of how much consumption the person would be will-

ing to forgo for more time) when all time is devoted to non-

market, noncrime activities. Second, the marginal return to

crime for the rst crime committed must exceed the indi-

vidual’s market wage. Thus, those who can command high

wages or those who place very high value on time devoted

to nonmarket/noncriminal uses will be the least likely to

engage in criminal activity.

Note, however, the observant criticism of Brown and

Reynolds (1973), showing that this clean conclusion is the

result of the modeling assumption that the baseline utility

is that of getting away with crime.

The model of Lee and McCrary (forth-

coming) emphasizes the dependence of this

conclusion on the time preferences of the

individual.

Intuitively, it seems like it would

be hard to deter an impatient individual

using a prison sentence, since most of the

disutility of a prison sentence is borne in the

future. Lee and McCrary propose modeling

crime using a modication of the basic job

search model in discrete time with an innite

horizon. Risk-neutrality is assumed, yet indi-

viduals in this model have very different

responses to the capture and punishment.

In their model, criminal opportunities are

independent draws from an identical “crim-

inal benet” distribution with distribution

function F(b) . The individual learns of an

opportunity each period and must decide

whether to take advantage of it. If the indi-

vidual engages in crime and is caught, she

is imprisoned for S periods, where S is an

independent draw from an identical sen-

tence-length distribution. As in the Becker

model, capture occurs with probability p .

If the individual abstains from crime, she

obtains ow utility a and faces the same

problem the next period. If she commits

crime and is not caught, she obtains ow util-

ity a + b and faces the same problem next

period. Finally, if she commits crime and is

caught, she obtains ow utility a − c for S

periods, before confronting the same prob-

lem at the conclusion of her sentence.

The criminal benet at which the individ-

ual is indifferent between crime and absten-

tion is given by

(6) b

∗

= c

___

1 − p

+ ν

{

___

1 − p

+ p

∫

∗

∞

(1 − F(z)) dz

}

Further details regarding the Lee and McCrary model

are given in McCrary (2010).

Journal of Economic Literature, Vol. LV (March 2017)

where ν = E

[

∑

s=1

S−1

]

is a summary param-

eter governing how the distribution of

sentences affects decision making and δ is

the discount factor.

As in Becker’s static model, crime is

reduced by increases in p and increases in c .

The added feature of this model, however,

is that crime is also reduced by increases in

sentence lengths, and this behavioral mech-

anism is modulated by time preferences.

As is intuitive, patient individuals are quite

responsive to increases to sentence lengths,

but impatient individuals show much more

muted responses. In the limit as the dis-

count factor approaches zero, the individ-

ual is arbitrarily more responsive to capture

than to punishment. The Lee and McCrary

model thus provides a simple way to reintro-

duce older ideas regarding the importance of

celerity into a Becker model.

Ultimately, the models proposed by Becker

and Ehrlich yield three main behavioral pre-

dictions: (1) the supply of offenses will fall as

the probability of apprehension rises, (2) the

supply of offenses will fall as the severity of

the criminal sanction increases, and (3) the

supply of offenses will fall as the opportunity

cost of crime rises. In other words, behav-

ioral changes can be brought about either

using carrots (better employment opportu-

nities) or sticks (criminal-justice inputs). The

following section connects these core pre-

dictions to the empirical literatures that have

For example, if we take S to be geometric, i.e., S has

support 1, 2, … , ∞ and P(S = s) = q (1 − q)

s−1

, where q is

the per period release probability, then standard results

using innite series show that ν = (1 − q) δ / (1 − (1 − q)δ) .

Interestingly, this shows that under a geometric distri-

bution for sentence lengths, reducing the probability of

release is equivalent to increasing the individual’s patience.

Equation (6) is not an explicit equation for b

∗

, but it

can be viewed as dening an implicit function. We can use

numerical methods to solve for b

∗

(e.g., Newton’s method

works well), and comparative statics are straightforward

using the implicit function theorem.

For related modeling ideas from criminology, see

Nagin and Pogarsky (2001), for example.

sought to test whether these predictions hold

in the real world.

2.2 Perceptions and Deterrence

Because economic models of offending

are microeconomic models that make pre-

dictions about individual behavior, our dis-

cussion of deterrence would be incomplete

without a discussion of how individuals

perceive risks and, especially, whether risk

perceptions mirror reality. Given the scope

of this review, our discussion of perceptions

is necessarily brief. We direct the reader to

an excellent review of this literature by Apel

(2013) for a more detailed accounting of the

perceptual-deterrence literature.

Perceptual deterrence is important because

the vast majority of the empirical-deterrence

literature operationalizes Becker’s model

of crime by studying the responsiveness of

crime to particular policy variables, such as

the number or productivity of police or the

punitiveness of sanctions. This approach

was initially borne out of the inadequacy of

data needed to test the microfoundations of

the Becker model, but has the advantage of

having generated a dense literature that is

practical and policy-relevant. Given that the

literature studies the effect of policy variables,

an important intermediate outcome and

indeed a precursor to identifying deterrence

is the extent to which potential offenders are

aware that policy has changed (Waldo and

Chiricos 1972, Nagin 1998, and Apel 2013).

Apel (2013) characterizes the link between

actual and perceived deterrence as involving

a series of considerations that include both

threat communication, the degree to which

a change in the certainty or the severity of a

sanction is communicated or advertised, and

risk perceptions, the individual’s perceived

risk of being apprehended and punished.

Crucially, risk perception is not assumed to

be stable and indeed an important litera-

ture has arisen that seeks to understand how

Chaln and McCrary: Criminal Deterrence: A Review of the Literature

offenders update risk perceptions in response

to experience (see Apel and Nagin 2011 for a

comprehensive review on the subject).

Ultimately, one of the most import-

ant questions for perceptual-deterrence

research is the degree of correspondence

between actual and perceived risks. If per-

ceptions closely mirror reality, then using

policy shocks to learn about the magnitude

of deterrence is straightforward. However,

to the extent that changes in policy often

go unnoticed by potential offenders, the

outcomes of policy research will tend to

be of limited value in studying deterrence.

Consider, for example, a policy that increases

the number of undercover police ofcers

who are assigned to patrol a city’s transit sys-

tem. Assuming that policy is unannounced

and, even if it is announced, that the news

does not easily trickle down to potential

offenders, it is difcult to imagine how

deterrence will accrue. It may well be that

the policy begins to be noticed by offenders

as they hear about cases in which undercover

ofcers have made arrests or if they have an

acquaintance who has been arrested in this

way. However, it seems likely that such infor-

mation will generate deterrence only via a

substantial temporal lag. Indeed, it seems

likely that a highly visible change in the num-

ber of uniformed ofcers or, alternatively, a

well-advertised policy to increase the num-

ber of undercover ofcers, will generate a

greater deterrence effect, even if the actual

intervention is no different.

The recent literature that links actual and

perceived risks is relatively small. Important

recent work includes that of Kleck et al.

(2005) and Kleck and Barnes (2013), who

conducted a telephone survey of 1,500 adults

in fty-four large urban counties in the

United States. They asked each individual to

estimate case clearance rates, the probability

of serving time in prison, and maximum sen-

tence for several different serious felonies.

Comparing perceived risks to actual risks,

they found little evidence of any correlations,

a nding that extends to police manpower

as well. Research by Lochner (2007) using

the National Longitudinal Survey of Youth

(NLSY) comes to a qualitatively similar con-

clusion reporting evidence of a signicant,

albeit weak, relationship between actual and

perceived risks of apprehension. Likewise, in

an application to drug use, a very common

crime resulting in arrest, MacCoun et al.

(2009) report that individuals living in states

that have decriminalized marijuana often do

not have any awareness of this and continue

to believe that they can be jailed for mari-

juana possession. These studies are charac-

terized by Apel (2013) as being discouraging

for deterrence research. However, as each

of the studies surveyed the general popula-

tion, most of whom are uninvolved in crime,

such research may have poor external valid-

ity. The best evidence on perceptions among

a sample of active offenders comes from

Lochner (2007), who reports that NLSY

youth who self-report criminal involvement

do, on average, have more accurate percep-

tions about arrest risks than noncriminally

involved youth.

A second strain of research considers

whether offenders change their risk percep-

tions in response to a past arrest. One avor of

this research has compared risk perceptions

among individuals who reported more fre-

quent arrest conditional upon offending (i.e.,

less successful offenders) to individuals who

reported fewer arrests per offense (i.e., more

successful offenders). This literature tends to

nd robust evidence of an association between

more frequent arrest and a higher perceived

probability of capture (Paternoster and

Piquero 1995, Piquero and Pogarsky 2002,

Pogarsky and Piquero 2003, and Carmichael

and Piquero 2006). A parallel literature has

found that risk perceptions are also informed

by the experience of acquaintances (Piquero

and Pogarsky 2002). Unfortunately, there are

a number of conceptual issues that make this

Journal of Economic Literature, Vol. LV (March 2017)

literature difcult to interpret. Most notably,

since these associations arise from cross-sec-

tional data, it is not possible to discern cause

from correlation. In particular, it is plausible

that more successful offenders have lower

perceived arrest probabilities for reasons that

are a function of personality and largely unre-

lated to experience. In response to this con-

cern, a more recent literature uses panel data

to measure “updating”—the idea that indi-

viduals change their prior risk perceptions on

the basis of whether or not they are appre-

hended in an earlier period. This literature

has also tended to nd robust evidence that

risk perceptions are sensitive to actual expe-

rience (Pogarsky, Piquero, and Paternoster

2004; Pogarsky, Kim, and Paternoster 2005;

Matsueda, Kreager, and Huizinga 2006; and

Anwar and Loughran 2011). Several more

specic ndings from this literature are

worth noting. First, while perceptions are

responsive to experience, offending is not

always responsive to perceptions, implying

that at least a portion of offending may be

idiosyncratic and perhaps undeterrable in

a stable policy regime. Second, less experi-

enced offenders are especially sensitive to

the experiences of peers, which is sensible

as they may not have sufcient history upon

which to draw conclusions. Third, there is

evidence that the general public, along with

less frequent offenders, tend to overestimate

their arrest risk and adjust their risk percep-

tions downward as they offend and recognize

that the risk of apprehension is lower than

they previously believed. An important cor-

ollary to this is that risk perceptions are more

sensitive to experience early in one’s crim-

inal career, with the deterrence value of an

arrest declining with experience (Anwar and

Loughran 2011).

Broadly speaking, the perceptual-deter-

rence literature provides several reasons to

be optimistic that meaningful deterrence

effects can exist and can be particularly

salient among younger offenders who have

yet to commit to a criminal career. The

best available evidence suggests that the

experience of arrest does lead to an increase

in the perceived likelihood of being appre-

hended for a future crime. What is less

clear is whether perceived risks change in

response to policy inputs that have more dif-

fuse impacts and whether advertising sanc-

tions can be a sufciently credible threat—a

proposition we discuss in further detail in the

subsequent empirical section of this paper.

2.3 Deterrence versus Incapacitation

Generally speaking, there are two mecha-

nisms through which criminal-justice policy

reduces crime: deterrence and incapacita-

tion. When by virtue of a policy change indi-

viduals elect not to engage in crime they

otherwise would have in the absence of the

change, we speak of the policy deterring

crime. On the other hand, a policy change

may also take offenders out of circulation as,

for example, with pretrial detention or incar-

ceration, preventing crime by incapacitating

individuals. The incapacitation effect can be

thought of as the mechanical response of

crime to changes in criminal-justice inputs.

While deterrence can arise in response to

any policy that changes the costs or benets

of offending, incapacitation arises only when

the probability of capture or the expected

length of detention increases.

The existence of incapacitation effects

has profound implications for the study of

deterrence. In particular, while research that

considers the effect of a change in the prob-

ability of capture will, generally speaking,

identify a mixture of deterrence and inca-

pacitation effects, research that considers

changes in the opportunity cost of crime is

more likely to isolate deterrence. Likewise,

while research on the effect of sanctions typ-

ically results in a treatment effect that is a

function of both deterrence and incapacita-

tion, clever research designs have been used

to identify the effect of an increase in the

Chaln and McCrary: Criminal Deterrence: A Review of the Literature

severity of a sanction that is unlikely to result

in an immediate increase in incapacitation.

For each literature discussed in this paper,

we provide a discussion of the degree to

which empirical estimates can be inter-

preted as providing evidence of deterrence

as distinct from incapacitation and, in some

cases, other behavioral effects. However, it is

important to note that deterrence is itself a

black box. In order to empirically observe a

behavioral response of crime to a particular

policy level, it must be the case that poten-

tial offenders perceive that the cost of com-

mitting a crime has changed (Nagin 1998,

Durlauf and Nagin 2011, and Nagin 2013).

Moreover, the behavioral response of crime

will depend on the accuracy of those percep-

tions. To wit, an intervention that success-

fully convinces potential offenders that the

expected cost of crime has increased, regard-

less of whether this is actually the case, will

likely reduce crime. The challenge for cost-ef-

fective public policy is to optimally trade off

between police and prisons so as to maximize

perceptual and, as such, actual deterrence.

3. Police and Crime

Becker’s prediction that the aggregate

supply of crime will be sensitive to society’s

investment in police arises from the idea that

an increase in police presence, whether it

is operationalized through increased man-

power or increased productivity, raises the

probability that an individual is apprehended

for having committed a particular offense. To

the extent that potential offenders are able to

observe an increase in police resources and

perceive a correspondingly higher risk to

criminal participation, crime is expected to

decline through the deterrence channel.

Empirically, the challenge for this litera-

ture is that changes in the intensity of polic-

ing are generally not random. As a result, it is

difcult to identify a causal effect of police on

crime using natural variation in policing. An

additional, more conceptual issue is that the

responsiveness of crime to police may also

reect an important role for incapacitation.

This arises from the idea that police tend

to reduce crime mechanically, even in the

absence of a behavioral response, by arrest-

ing offenders who are subsequently incar-

cerated and incapacitated.

The extent to

which investments in police are cost effective

depends, in large part, on the degree to which

police deter rather than simply incapacitate

offenders. In this section, we consider the

responsiveness of crime to both police man-

power and police tactics, broadly dened. For

each literature, we discuss the challenges with

respect to both econometric identication as

well as interpretation of the resulting parame-

ters as evidence in favor of deterrence.

3.1 Police Manpower

A large literature has used city- or state-

level panel data and, recently, a variety of

quasi-experimental designs to estimate the

elasticity of crime with respect to police

manpower.

This literature is ably summa-

rized by Cameron (1988), Nagin (1998),

Eck and Maguire (2000), Skogan and Frydl

(2004), and Levitt and Miles (2006), all of

whom provide extensive references.

The early panel-data literature tended to

report small elasticity estimates that were

rarely distinguishable from zero and some-

times even positive, suggesting perversely

that police increase crime.

The ensuing

In this context, deterrence can arise either from a

general decrease in offending or from a shift towards less

productive but correspondingly less risky modes of offend-

ing—for example, a shift from robbery to larceny.

This elasticity can be thought of as a reduced-form

parameter that captures both deterrence effects as sug-

gested by neoclassical economic theory, as well as incapac-

itation effects that arise when offenders are incarcerated

and thus constrained in their ability to offend.

Papers in this literature employ a wide variety of

econometric approaches. Early empirical papers such as

Ehrlich (1973) and Wilson and Boland (1978) focused on

the cross-sectional association between police and crime.

Journal of Economic Literature, Vol. LV (March 2017)

discussion in the literature was whether police

reduce crime at all. Beginning with Levitt

(1997), an emerging quasi-experimental liter-

ature has argued that simultaneity bias is the

culprit for the small elasticities in the panel-

data literature.

The specic concern articu-

lated is that if police are hired in anticipation

of an upswing in crime, then there will be a

positive bias associated with regression-based

strategies, masking a true negative elasticity.

The recent literature has therefore generally

focused instead on instrumental variables (IV)

strategies designed to overcome this bias.

The rst plausible instrumental vari-

able to study the effect of police manpower

on crime was proposed by Levitt (1997).

Leveraging data on the timing of mayoral

and gubernatorial elections, Levitt provides

evidence that in the year prior to a munici-

pal or state election, police manpower tends

to increase, presumably due to the desire

of elected ofcials to appear to be “tough

on crime.” The exclusion restriction is that,

but for increases in police manpower, crime

does not vary cyclically with respect to the

election cycle. Using data from fty-seven

cities spanning 1972–97, Levitt reports very

small least-squares estimates of the effect

of police and crime that are consistent with

the prior literature. However, IV estimates

are large and economically important, with

elasticities ranging from moderate in magni-

tude for property crimes (−0.55 for burglary

and −0.44 for motor vehicle theft) to large in

magnitude for violent crimes such as robbery

(−1.3) and murder (−3). Ultimately, follow-

ing a reanalysis of the data by McCrary (2002),

the IV coefcients reported by Levitt were

found to be insignicant after a problem with

weighting was addressed. The insignicance

of the coefcients is ultimately driven by the

Some of the leading examples of quasi-experimental

papers are Levitt (2002), Di Tella and Schargrodsky (2004),

Klick and Tabarrok (2005), Evans and Owens (2007), Lin

(2009), and Machin and Marie (2011).

fact that the rst-stage relationship between

election cycles and police hiring is weak,

complicating both estimation and inference.

Levitt (1997) has given rise to a series of

related papers that seek to identify a national

effect of police manpower on crime by isolat-

ing conditionally exogenous within-city vari-

ation in police stafng levels. These papers

include Levitt (2002), which uses variation

in reghter numbers as an instrument for

police manpower; Evans and Owens (2007),

who instrument for police manpower using

the size of federal Community Oriented

Policing Services (COPS) grants awarded

to cities to promote police hiring; and Lin

(2009), who instruments for changes in police

manpower using the idea that US states

have differential exposure to exchange-rate

shocks depending on the export intensity

of local industry. These strategies consistently

demonstrate that police do reduce crime.

However, the estimated elasticities display a

wide range, roughly −0.1 to −2, depending

on the study and the type of crime. Moreover,

relatively few of the estimated elasticities are

signicant at conventional levels of condence,

reecting a great deal of sampling variability

and the use of relatively weak instruments. In

many cases, extremely large elasticities (i.e.,

those larger than one in magnitude) cannot

be differentiated from zero. Overall, Chaln

and McCrary (forthcoming) characterize

the pattern of the cross-crime elasticities as,

in general, favoring a larger effect of police

on violent crimes than on property crimes,

with especially large effects of police on

murder, robbery, and motor vehicle theft.

Notably, Worrall and Kovandzic (2007) report no

reduced-form relationship between COPS grants and

crime. However, their analysis is based on a smaller sample

of cities than the analysis of Evans and Owens (2007).

This pattern is found in several prominent panel

data papers, in particular Levitt (1997), Evans and Owens

(2007), and Chaln and McCrary (forthcoming), each of

which report especially large elasticity estimates for mur-

der (−0.6 to −0.8) and robbery (−0.5 to −1.4).

Chaln and McCrary: Criminal Deterrence: A Review of the Literature

A second noteworthy contribution to the

modern police manpower literature is that of

Marvell and Moody (1996), who leverage the

concept of Granger causality to explore the

extent to which police manpower is, in fact,

responsive to changes in crime. The motiva-

tion behind such an approach is that if crime

is responsive to lagged police but police staff-

ing is not responsive to lagged crime, then

the case for instrumental variables is weak-

ened considerably. Finding no evidence of

a link between lagged crime rates and cur-

rent police stafng levels at either the state

or city level, Marvell and Moody estimate

the responsiveness of crime to police using

a standard two-way xed-effects model and

report elasticities that are fairly small in mag-

nitude (ranging from −0.15 for burglary to

−0.30 for motor vehicle theft) and are more

consistent with the early least-squares litera-

ture than the IV literature that has prolifer-

ated in recent years.

Ultimately, the Granger causality exer-

cise is subject to the same omitted variables

bias concerns that plague any least squares

regression model, and is therefore of dubious

value in establishing causality. Nevertheless,

the weak evidence of a link between lagged

crime and current police stafng presented

in Marvell and Moody is, in our view, under-

appreciated. Given the large discrepancy

between Marvell and Moody’s estimates and

those in Levitt (1997), which use the same

underlying data, one of two propositions

must be true: (1) Marvell and Moody’s esti-

mates of the effect of lagged crime on police

manpower are biased due to the exclusion of

important omitted variables, or (2) There is no

simultaneity bias between police and crime—

discrepancies between least squares and IV

estimates are instead driven by measurement

errors in either police stafng or measures of

UCR index crimes. This is an idea that is dealt

with in detail in Chaln and McCrary (forth-

coming). Leveraging two potentially inde-

pendent measures of police manpower (one

from the FBI’s Uniform Crime Reports and

another from the US Census’s Annual Survey

of Government Employment) for a sample of

242 US cities over a fty-one-year time period,

Chaln and McCrary construct measurement

error corrected IV models using one measure

of police as an instrument for the other. Their

principal nding is that elasticities reported in

the recent IV literature can be replicated by

simply correcting for measurement errors in

police data and without explicitly addressing

the possibility of simultaneity bias. The result-

ing implication is that Marvell and Moody’s

basic inference regarding the lack of causality

running from crime to police manpower may

be correct. A related contribution in Chaln

and McCrary is to estimate police elasticities

with remarkable precision, reporting elastici-

ties of − 0.67 ± 0.48 for murder, − 0.56 ± 0.24

for robbery, − 0.34 ± 0.20 for motor vehicle

theft, and − 0.23 ± 0.18 for burglary.

While the majority of the police manpower

literature uses aggregate data, there is a cor-

responding literature that assesses the impact

of police on crime using natural experiments

in a particular jurisdiction. An early account

of such a natural experiment is found in

Andenaes (1974), who documents a large

increase in crime in Nazi-occupied Denmark

after German soldiers dissolved the entire

Danish police force (Durlauf and Nagin

2011 and Nagin 2013). Modern literature

has found similarly large effects. In particu-

lar, DeAngelo and Hansen (2014) document

an increase in trafc fatalities that occurred

in the aftermath of a budget cut in Oregon

that resulted in a mass layoff of state troop-

ers. Similarly, Shi (2009) reports an increase

in crime in Cincinnati, OH, in the aftermath

of an incident in which police used deadly

force against an unarmed African American

teenager.

As Shi (2009) notes, the police response to the riot was

to reduce productivity disproportionately in riot-affected

neighborhoods.

Journal of Economic Literature, Vol. LV (March 2017)

3.2 Police Deployment and Tactics

The police manpower literature is informa-

tive with respect to the aggregate response of

crime to increases in police stafng. However,

the aggregate manpower literature leaves

many interesting and important questions

unanswered. In particular, to what extent do

the estimated elasticities reect deterrence?

Likewise, what is the specic mechanism

that leads to deterrence? If the mechanism

is based on perceptual deterrence—the idea

that offenders observe an increase in police

presence and adjust their behavior accord-

ingly—then it should be the case that offend-

ing is especially sensitive to large and easily

observed changes in police deployment and

tactics. To address these questions, a related

literature that is found mostly in criminol-

ogy has studied the effect of changes in the

intensity of policing on crime with a distinct

focus on the crime-reducing effect of vari-

ous “best practices.” In particular, declines in

crime that are not attributable to spatial dis-

placement have been linked to the adoption

of “hot spots” policing (Sherman and Rogan

1995, Sherman and Weisburd 1995, Braga

2001, Braga 2005, Weisburd 2005, Braga

and Bond 2008, and Berk and MacDonald

2010), “problem-oriented” policing (Braga

et al. 1999; Braga et al. 2001; and Weisburd

et al. 2010), and a variety of other proactive

approaches. Similarly, a large research lit-

erature that has examined the local impact

of police crackdowns has consistently found

large and immediate (but typically not last-

ing) reductions in crime in the aftermath of

hyper-intensive policing (Sherman 1990).

Such ndings are further supported by

evidence from several informative natural

experiments that have identied plausibly

exogenous variation in the intensity of polic-

ing. Three prominent examples are Klick

and Tabarrok (2005), who study the effect

of police redeployments in Washington, DC,

that result from shifts in terror alert levels;

Di Tella and Schargrodsky (2004), who study

the effect of a shift in the intensity of policing

in certain areas of Buenos Aires after a 1994

synagogue bombing; and Draca, Machin,

and Witt (2011), who study police redeploy-

ments in the aftermath of the 2005 London

tube bombings.

The literature on police deployments and

tactics has focused predominantly on three

types of interventions. The rst is an inno-

vation commonly referred to as “hot-spots”

policing. As the moniker suggests, hot-

spots policing describes a strategy in which

police are disproportionately deployed to

areas in a city that appear to attract dispro-

portionate levels of crime.

The second

type of intervention is often referred to as

“ problem-oriented” policing. This term

is used broadly and refers to a collection

of focused deterrence strategies that are

designed to change the behavior of specic

types of offenders or to be successful in spe-

cic jurisdictions. A nal intervention that

has received attention in the literature is that

of “proactive” policing. Proactive policing

refers to strategies that are deigned to make

policing more intensive, holding resources

xed. The idea can be traced back to the

concept of “broken windows,” or disorder

policing introduced by Wilson and Kelling

(1982) and refers to the notion that, just like

xing a broken window sends a message to

would-be vandals that the community cares

about maintaining social order, arresting

individuals for relatively minor infractions

sends a message to potential offenders that

the police are watchful.

The idea that crime hot spots might exist is immedi-

ately obvious to many and can be found in the academic

literature at least as far back as Shaw and McKay (1942).

Modern research has linked criminal activity to specic

types of places such as bars (Roman and Reid 2012) and

apartment buildings, as well as to places that lack formal or

informal guardians (Eck and Weisburd 1995).

Chaln and McCrary: Criminal Deterrence: A Review of the Literature

3.2.1 Hot-Spots Policing

We begin with a discussion of hot-spots

policing, which we distinguish from aggre-

gate police manpower research for several

reasons. First, as the manpower literature

largely uses city-level variation, it identies

the effect of adding police at the expense of

some other type of public input. In contrast,

hot-spots policing involves a reallocation of

existing resources. Such a strategy is advan-

tageous, as it does not require a change in

current outlays. However, it also leaves open

the possibility that moving police around

merely shifts, rather than reduces, crime.

In order for hot-spots policing to be a

viable crime reduction strategy, two condi-

tions must be met. First, given resource con-

straints, the feasibility of such a deployment

strategy relies on crime being sufciently

concentrated in a relatively small number of

hot spots. Second, hot spots must be suf-

ciently stable such that the spatial distribu-

tion of crime in the absence of a change in

police deployment can be predicted with a

reasonable degree of accuracy. Hence, the

adoption of hot-spots policing must begin

with an accounting of the geographic con-

centration of crime, as well as an assessment

of the extent to which hot spots are per-

manent as opposed to transitory. Sherman

(1995) captures both of these ideas, charac-

terizing crime hot spots as “small places in

which the occurrence of crime is so frequent

that it is highly predictable, at least over a

one-year period.”

A seminal paper by Sherman, Gartin, and

Buerger (1989) is the rst to provide descrip-

tive data on the degree to which crime is

spatially concentrated. Using data from

Minneapolis, Sherman and coauthors found

that just 3 percent of addresses and inter-

sections in Minneapolis produced 50 per-

cent of all calls for service to the police. This

nding is echoed by Weisburd, Maher, and

Sherman (1992) and in more recent papers by

Weisburd et al. (2004) and Weisburd, Morris,

and Groff (2009), which report that a very

small percentage of street segments in Seattle

accounted for 50 percent of crime incidents

for each year over a fourteen-year period.

With respect to predictability, Weisburd et al.

(2004), using the same data from Seattle, used

trajectory analysis to establish that hot spots

tended to be highly persistent, often persist-

ing for many years.

Naturally, the observation that crime is

so highly concentrated in a very small num-

ber of places has led to efforts to inten-

sify the focus of police resources on these

places. These interventions have, in turn,

led to a corresponding experimental and

quasi-experimental research literature that

seeks to evaluate the efcacy of such strat-

egies. The rst-order question that the hot-

spots policing literature seeks to address

involves the degree to which highly local-

ized crime is responsive to a change in the

intensity of policing. By responsive, crim-

inologists generally refer to the idea that

crime declines in local areas that have been

exposed to more intensive patrol without

merely inducing equivalent spillovers to

untreated adjacent areas. However, we note

that while spillovers undermine the viability

of hot-spots policing as a crime-reduction

strategy, they nevertheless constitute evi-

dence of responsiveness and, as such, are

useful in identifying deterrence. Moreover,

a particular feature of this research makes it

especially salient for the study of deterrence

(Nagin 2013). Notably, while the literature

tends to nd that intensive policing reduces

crime, elements of intensive policing such

as rapid response times do not appear to

increase the likelihood of an arrest (Spelman

An excellent review of this literature may be found in

Weisburd, Bruinsma, and Bernasco (2009).

Hot spots can, of course, also be temporary. An excel-

lent accounting of efforts to predict temporary hot spots in

Pittsburgh can be found in Gorr and Lee (2015).

Journal of Economic Literature, Vol. LV (March 2017)

and Brown 1981). Such a pattern in the data

tends to be consistent with deterrence but

not with incapacitation.

The rst test of policing crime hot spots

may be found in a 1995 randomized experi-

ment conducted by Sherman and Weisburd

in Minneapolis. The experiment tested

whether doubling the intensity of police

patrols in crime hot spots resulted in a

decrease in crime and found that crime

declined by approximately 10 percent in

experimental places relative to control places.

No evidence of crime displacement—that is,

spillovers—was found. Findings in Sherman

and Weisburd (1995) have, to a large extent,

been replicated in other places and contexts

including the presence of open-air drug

markets and “crack houses” (Hope 1994,

Weisburd and Green 1995, and Sherman et

al. 1995), violent crime hot spots (Sherman

and Rogan 1995, Braga et al. 1999, and Caeti

1999), and places associated with substan-

tial social disorder (Braga and Bond 2008

and Berk and MacDonald 2010). Indeed, a

review of the literature by Braga (2001) iden-

tied nine experiments or quasi-experiments

involving hot-spots policing and noted that

seven of the nine studies, including a majority

of the randomized experiments, found evi-

dence of signicant and large crime reduc-

tions. Notably, a majority of the literature

nds no evidence of displacement of crime

to adjacent neighborhoods (Weisburd et al.

2006), while a number of studies have found

that the opposite is true—that there tends

to be a diffusion of benets to nontreated

adjacent places (Sherman and Rogan 1995,

Braga et al. 1999, and Caeti 1999).

Both of

these ndings are perfectly consistent with

our conceptualization of deterrence.

An excellent review of the theory and empirical nd-

ings regarding displacement in this literature can be found

in Weisburd et al. (2006).

3.2.2 Problem-Oriented Policing

Intensive policing of hot spots is one way

that police potentially deter crime. Another

broad deterrence-based strategy is that of

problem-oriented policing. Broadly speak-

ing, this strategy entails engaging with

community residents to identify the most

salient local crime problems and designing

strategies to deter unwanted behavior. The

specics are highly variable by design and

are intended to leverage local resources

to address highly local concerns. What

these strategies have in common and why

they are frequently referred to as “focused

deterrence” strategies is that each of them

attempts to generate deterrence through

advertising (Zimring and Hawkins 1973).

The idea is to create deterrence by making

potential offenders explicitly aware of the

risks of serious criminal involvement.

Undoubtedly the most well-known evalua-

tion of a problem-oriented policing approach

is that of Boston’s Operation Ceasere by

Kennedy et al. (2001). The stated purpose

of Ceasere was to reduce youth gun vio-

lence in Boston. The intervention involved a

multifaceted approach and included efforts

to disrupt the supply of illegal weapons to

Massachusetts. It also included messages

communicated by police directly to gang

members that authorities would use every

available “lever” to punish gangs collectively

for violent acts committed by individual gang

members. In particular, police indicated that

the stringency of drug enforcement would

hinge on the degree to which gangs used vio-

lence to settle business disputes. The result of

the intervention was that youth violence fell

considerably in Boston relative to other US

cities included in the study.

Indeed, the perception of Ceasere

has been overwhelmingly positive and

accordingly it has given rise to a number

of similarly motivated strategies that are

collectively referred to as “pulling levers.”

Chaln and McCrary: Criminal Deterrence: A Review of the Literature

Prominent evaluations of pulling-levers

interventions include research carried out

in Richmond, VA (Raphael and Ludwig

2003), Indianapolis (McGarrell et al. 2006),

Chicago (Papachristos, Meares, and Fagan

2007), Stockton, CA (Braga 2008b), Lowell,

MA (Braga et al. 2008), High Point, NC

(Corsaro et al. 2012), Nashville (Corsaro

and McGarrell 2010), Cincinnati (Engel,

Corsaro, and Tillyer 2010), and Rockford,

IL (Corsaro, Brunson, and McGarrell 2010).

Researchers have also evaluated a multi-

city pulling-levers strategy known as Project

Safe Neighborhoods (PSN), which enlisted

the cooperation of federal prosecutors to

crack down on gun violence. A 2012 review

of the literature by Braga and Weisburd

suggests that pulling-levers strategies have

been effective in reducing serious violent

crime, with all reviewed studies nding neg-

ative point estimates, the majority of which

were signicant. With respect to individual

evaluations, reductions in crime have been

found in High Point, Chicago, Indianapolis,

Stockton, Lowell, Nashville, and Rockford

and null ndings have been found in

Richmond and Cincinnati.

With respect to

Project Safe Neighborhoods, the research is

promising but not denitive. McGarrell et

al. (2010) report that declines in crime were

greater in PSN cities than in non-PSN cities.

However, there is a great deal of heterogene-

ity among cities, making it difcult to draw

clear inferences.

On the whole, evaluations of pulling-

levers strategies produce promising results,

though inference is invariably complicated

by a lack of randomized experiments and

the inherent difculty in identifying appro-

priate comparison cities. Identication

problems are additionally compounded by

the difculty in identifying mechanisms, as

Braga and Weisburd (2012) provided an excel-

lent review of the literature including a comprehensive

meta-analysis of the research ndings.

each pulling-levers strategy is complex, mul-

tifaceted, and situation dependent, often

involving changes in both the intensity of

law enforcement as well as sentencing (e.g.,

Project Exile in Richmond, VA, as well as

Project Safe Neighborhoods). Accordingly,

it is easy to imagine that as additional

resources are brought to bear, some of the

effects of pulling-levers strategies might

accrue via incapacitation effects. Concerns

regarding identication have led Skogan and

Frydl (2004) to conclude that such research

is “descriptive rather than evaluative.” Given

the relatively large effect sizes reported in

the literature, our reading of these papers

is more optimistic than that of Skogan and

Frydl. However, caution is warranted in char-

acterizing this literature as having detected

unassailable evidence of deterrence.

3.2.3 Proactive and Disorder Policing

A nal strand of the police tactics litera-

ture in criminology investigates the respon-

siveness of crime to the intensity of policing,

holding resources constant, an idea that is

generally referred to as “proactive” policing.

As there is no standardized way to assess the

extent to which individual police departments

engage in police work that is proactive, in

practice, this literature seeks to understand

if the intensity of arrests for minor infrac-

tions has an effect on the incidence of more

serious crimes. Building on a proposition in

Wilson and Kelling (1982), such an empiri-

cal operationalization was rst proposed by

Sampson and Cohen (1988) and has been

replicated to various degrees by MacDonald

(2002) and Kubrin et al. (2010). The general

strategy is to regress crime rates on a mea-

sure of policing intensity. In practice, polic-

ing intensity has been operationalized using

the number of driving under the inuence

(DUI) and disorderly conduct arrests made

per police ofcer. Using this approach has,

in some cases, led to ndings that are con-

sistent with a deterrence effect of proactive

Journal of Economic Literature, Vol. LV (March 2017)

policing. However, in the best controlled

models, coefcients on the proactive policing

proxy become small and insignicant. More

importantly, these models are plagued by

problems of simultaneity bias, omitted vari-

ables, and the inevitable difculty involved

in nding a credible proxy for the concept of

proactive policing, as opposed to simply an

environment that is rich in opportunities for

police ofcers to make arrests.

A second focus of the literature has been

on the advent of broken-windows policing

(also known as “order maintenance” or “dis-

order” policing, a policy innovation proposed

by Wilson and Kelling 1982). The idea behind

broken-windows policing is that police can

affect crime through tough enforcement of

laws governing relatively minor infractions

such as vandalism and turnstile jumping.

Broken-windows policing, in theory, operates

primarily through perceptual deterrence—if

offenders observe that police are especially

watchful, they may update their perceived

probability of apprehension for a more serious

crime and accordingly will decrease their par-

ticipation in crime. In the popular media, bro-

ken-windows policing is an idea that is heavily

associated with Mayor Rudolph Giuliani and

New York Police Commissioner William J.

Bratton, who has attributed the dramatic

decline in crime in New York City after 1990

to its rollout (Kelling and Bratton 2015).

A corresponding research literature

has arisen to evaluate the effectiveness of

broken-windows policing—in practice, this

literature has focused disproportionately

on the experience of New York City, which

experienced the largest decline in crime

among major US cities. This literature pro-

duces mixed ndings. On the one hand, time

series analyses by Kelling and Sousa (2001)

and Corman and Mocan (2005) nd that

misdemeanor arrests are negatively asso-

ciated with future arrests for more serious

crimes such as robbery and motor vehicle

theft. On the other hand, later research has

pointed out that these studies omit a con-

trol group and has tended to focus on the

fact that New York’s aggregate crime trends,

while steeper, are broadly similar to those of

other cities that did not institute a policy of

broken-windows policing (Eck and Maguire

2000; Rosenfeld, Fornango, and Baumer

2005; and Harcourt and Ludwig 2006).

The two most credible analyses, those of

Harcourt and Ludwig (2006) and Rosenfeld,

Fornango, and Rengifo (2007), use

precinct-level data on misdemeanor arrests

and violations and nd either no effect or

very small effects. More fundamentally, there

are a number of alternative explanations for

New York’s dramatic reduction in crime

including the receding of the crack epidemic

(Blumstein 1995), changes in demographics

that are poorly measured at lower levels of

geographic granularity, general strategies

to address disorder such as boarding aban-

doned buildings, and the implementation of

the data-driven Compstat system (Weisburd

et al. 2003). Accordingly, even if identica-

tion problems can be set aside, it is unclear

that this literature can isolate the impact of

disorder policing from other changes that

drove crime down in New York City. Given

the dramatic rollback of the New York City

Police Department’s “stop-and-frisk” policy

in 2014 and the continued decline in serious

crime, as well as the failure of the most care-

ful studies to nd evidence of large effects,

we are skeptical that disorder policing has

played a large role in the decline in crime

in New York City.

Overall, our reading of

this literature is that the evidence in favor of

an important effect of proactive policing on

crime is weak.

Broken-windows policing and the associated stop-

and-frisk policy implemented by the New York City police

department has generated substantial public controversy.

A 2009 paper by Fagan et al. (2009) provides evidence of

the demographic burden of such policies that is dispropor-

tionately borne by African Americans.

Chaln and McCrary: Criminal Deterrence: A Review of the Literature

Of course, New York City is not the only

municipality to experiment with disorder

policing and, in our view, some of the stron-

gest evidence can be found in research

from other cities. Three papers that employ

especially strong research designs are worth

mentioning. Braga et al. (1999) provides the

rst experimental evaluation of a strategy

designed explicitly to address disorder. In

Jersey City, NJ, twelve of twenty-four crime

hot spots were randomly assigned to receive

an intervention that involved disorder polic-

ing as well as other place-specic treat-

ments that were intended to reduce crime.

Such treatments include clearing vacant

lots, requiring store owners to clean store

fronts, and facilitating more frequent trash

removal. Treated places experienced large

declines in both crime and calls for service.

In a follow-up study in Lowell, MA, Braga

and Bond (2008) attempted to further isolate

disorder policing from other types of disor-

der reduction, randomly assigning seventeen

Lowell hot spots to receive a general disor-

der policing strategy. This study also showed

strong reductions in crime in treated areas.

However, the greatest gains were found in

areas with an especially heavy focus on sit-

uational crime prevention, as opposed to

arresting larger numbers of low-level offend-

ers. Evidence in favor of an effect of misde-

meanor arrests is far more limited. Finally,

a particularly careful paper by Caetano and

Maheshri (2014) nds no evidence of an

effect of “zero tolerance” law enforcement

policies on crime using microdata from

police precincts in Dallas. Taken as a whole,

the evidence suggests that reducing disorder

is a promising strategy for controlling crime.

However, it is difcult to characterize these

reductions as deterrence. In particular, disor-

der reduction may simply help people to feel

better about their neighborhoods, thus rep-

resenting a shift in preferences, rather than

movement along the curve that is induced

by an increase in the perceived probability

of capture by police. We remain skeptical

that disorder policing provides evidence of

deterrence.

3.2.4 Changes in City-Wide Police

Deployments

The ubiquity of the hot spots, problem-

oriented and proactive policing literatures

in criminology has spawned a parallel liter-

ature in economics that seeks to learn from

natural experiments in police deployments.

This literature is conceptually similar to the

hot-spots literature with two exceptions.

First, the identifying variation is naturally

occurring in contrast to experimental manip-

ulation, which may be excessively contrived.

Second, several of the natural experiments

identify the impact of a diffuse reduction

in resources, rather than a concentration of

resources at particular hot spots.

Three prominent studies are those of

DiTella and Schargrodsky (2004); Klick and

Tabarrok (2005); and Draca, Machin, and

Witt (2011). Each of these studies lever-

ages a redeployment of police in response

to a perceived terrorist threat. The appeal

of these studies is that terrorist threats are

plausibly exogenous with respect to trends

in city-level crime and therefore represent

a unique opportunity to learn about the

response of crime to changes in normal rou-

tines of policing. Di Tella and Schargrodsky

study the response of police in Buenos Aires

to the 1994 bombing of a Jewish community

center. In the aftermath of the bombing,

Argentine police engaged in a strategy of

“target hardening” synagogues by deploy-

ing disproportionate numbers of ofcers to

blocks with synagogues or other buildings

housing Jewish organizations. Di Tella and

Schargordsky report that the intervention

led to a large decline in motor vehicle thefts

on the blocks that received additional police

patrols though the effects. Notably, this result

has been called into question by Donohue,

Ho, and Leahy (2013), who reanalyzed

Journal of Economic Literature, Vol. LV (March 2017)

the original data and report evidence that

is more consistent with spatial displace-

ment of crime rather than crime reduction.

However, with respect to identifying behav-

ioral changes among offenders, both stories

are equally consistent with deterrence. In

a similar study, Klick and Tabarrok (2005)

utilize the fact that when terror alert levels

set by the US Department of Homeland

Security rise, property crime (but not violent

crime) tends to fall in Washington DC, with

especially large declines in areas that receive

the largest redeployments of police protec-

tion. With respect to the United Kingdom,

Draca, Machin, and Witt (2011) study the

2005 London tube bombings which resulted

in sizable shifts in the deployments of police

from the suburbs to central London and

nd that “street crimes” such as robbery and

theft are reduced considerably in areas that

received additional ofcers.

With respect to studying variation in the

spatial concentration of police, two additional

papers are worth noting. Cohen and Ludwig

(2003) exploit short-term variation in the

intensity of police patrols by day of the week

in several different Pittsburgh patrol areas.

They found that shootings were consider-

ably lower in areas and on days that received

more intensive police patrols. With respect

to the long-term consequences of patterns of

police deployments, MacDonald, Klick, and

Grunwald (2016) use a spatial regression dis-

continuity (RD) design to study the impact

of especially intensive policing around the

University of Pennsylvania, a large urban uni-

versity campus. In particular, areas directly

adjacent to the university received police

patrols from both the university and munic-

ipal police. Areas slightly further away from

the campus received only municipal police

patrols. The nding is that street crimes are

substantially higher in the blocks just outside

the area patrolled by the university police

relative to the blocks just inside the univer-

sity patrol area.

3.3 Deterrence versus Incapacitation

The literature has reached a consensus

that increases in police manpower reduce

crime, at least for a population-weighted

average of US cities. With respect to police

deployments and tactics, the literature sup-

ports the idea that crime is responsive to a

visible police presence in hot spots and pull-

ing-levers strategies that advertise deter-

rence, while evidence in favor of an effect

of proactive policing strategies such as bro-

ken windows and disorder policing is more

suspect. A remaining issue is to address the

degree to which each of these literatures is

informative with respect to disentangling

deterrence from incapacitation.

With respect to the aggregate manpower

literature, Levitt (1998) provides the rst

attempt to systematically unpack the rela-

tionship between deterrence and incapac-

itation by empirically examining the link

between arrest rates and crime, a rela-

tionship that is negative. Levitt posits that

this negative relationship can be explained

either by deterrence, incapacitation, or

measurement errors in crime. Ruling out

measurement errors as a likely culprit, he

differentiates between deterrence and

incapacitation using the effect of changes

in the arrest rate for one crime on the rate

of other crimes.

As Levitt notes, “in con-

trast to the effect of increased arrests for

one crime on the commission of that crime,

where deterrence and incapacitation are

indistinguishable, it is demonstrated that

these two forces act in opposite directions

when looking across crimes. Incapacitation

suggests that an increase in the arrest rate

for one crime will reduce all crime rates;

Utilizing an insight from Grilliches and Hausman

(1986)—that measurement errors should yield the greatest

bias in short-differenced regressions—Levitt (1998) com-

pares regression estimates of the relationship between

crime and arrest rates using short- and long-differences,

nding similar effects.

Chaln and McCrary: Criminal Deterrence: A Review of the Literature

deterrence predicts that an increase in the

arrest rate for one crime will lead to a rise

in other crimes as criminals substitute away

from the rst crime.” Levitt concludes that

deterrence appears to be the more import-

ant factor, particularly for property crimes.

Owens (2013) reports a similar nding,

examining whether variation in police staff-

ing resulting from the COPS hiring pro-

gram led to increased arrests. Despite the

fact that the program, which provided fund-

ing to increase the number of patrol ofcers

in US cities, appears to have led to a decline

in crime, no signicant effect is found on

arrests. As a result, Owens concludes that

there is little evidence in favor of incapac-

itation, which necessarily must operate

through arrests, thus implying a large role

for deterrence.

While analyses by Levitt (1998) and

Owens (2013) are suggestive of a meaning-

ful role for manpower-induced deterrence,

it is nevertheless difcult to disentangle

deterrence from incapacitation in this way.

In particular, a null relationship between

police and arrests is also consistent with

the idea that police productivity decreases

when there are fewer crimes to investi-

gate. Moreover, the imprecise parameter

estimates on arrest along with standard

errors that are not trivial in size in Owens

(2013) render it difcult to make strong

claims regarding the null effect of police

on arrests. For this reason, while the aggre-

gate-data literature is ideal for understand-

ing the overall relationship between police

and crime, it is only somewhat informative

with respect to the magnitude of deter-

rence. This point is further compounded

by the observation that research has yet to

document the degree to which offenders

perceive or are aware of increases in police

manpower (Nagin 1998).

We suspect that the literature on police

tactics is considerably more informative

with respect to identifying deterrence. In

particular, offenders are more likely to be

aware of an enhanced police presence in

small, local areas than relatively small changes

in the number of police in a city spread out

over a large geographic area. Likewise, while

offenders tend to commit crimes locally, in

order for incapacitation to explain the large

declines in crime that occur in hot spots, it

would have to be the case that offending

is so local so as to be specic to a group of

one or two blocks. The large drops in crime

that occur in crime hot spots after they are

more aggressively policed is more consistent

with deterrence than with incapacitation.

Focused deterrence strategies are also par-

ticularly informative in that declines in crime

have been shown to be specic to the focus

of the intervention. To the extent that at least

some offenders are generalists, rather than

specialists who commit only a certain type

of offense, such a pattern is more consistent

with deterrence than with incapacitation.

In sum, while it remains possible that an

increased police presence lowers crime by

situating police ofcers in locations where

they are more likely to arrest and incapac-

itate potential offenders, on the whole, the

high degree of visibility around police crack-

downs or hot spots policing suggests a poten-

tially greater role for deterrence.

4. Sanctions and Crime

A second idea in Becker’s neoclassical

model of offending is that crime will be

responsive to the certainty and severity

An important exception to this intuition, however,

can be found in Mastrobuoni (2013), who studies the

responsiveness of crime to regular shift changes among

the various police forces in Milan. Matsrobuoni nds that

despite large temporal discontinuities in clearance rates

during shift changes, robbers do not appear to exploit these

opportunities and concludes that there is only limited evi-

dence of deterrence. A remaining question is the extent to

which the result depends on the ability of potential offend-

ers to accurately perceive these discontinuities.

Journal of Economic Literature, Vol. LV (March 2017)

of punishment.

Accordingly, a parallel

literature considers the responsiveness of

crime to the harshness of criminal sanctions,

along both the intensive and extensive mar-

gin. Three literatures, in particular, are worth

mentioning. First, a series of papers consid-

ers the effect of sentencing policy generally

or, alternatively, sentence enhancements on

crime, to test the prediction that crime will

decrease in response to a sanction regime

that either raises the probability of a prison

sentence or raises the length of a prison sen-

tence, if given. In practice, this literature

focuses primarily on the intensive margin,

that is, the severity of punishment rather than

the probability that a custodial punishment

is given conditional upon being arrested. A

corresponding literature considers the effect

of laws that govern the age of criminal major-

ity and, as such, generate large and pervasive

discontinuities in the sanctions that individ-

ual offenders face. Since adult sanctions are

more intensive along both the intensive and

extensive margins, such studies identify a

reduced-form deterrence effect that does not

explicitly differentiate between the certainty

and the severity of punishment. Finally, a

particularly prominent literature considers

the effect of a capital-punishment regime or

the incidence of executions on murder. Since

executions enhance the expected severity of

the sanction without directly affecting an

offender’s probability of capture, this liter-

ature is potentially compelling with respect

to understanding deterrence as, subject to

satisfying the standards of econometric iden-

tication, it allows for the isolation of a pure

The term “certainty of punishment” is often used in

the literature to refer either to the probability that an indi-

vidual is apprehended or to the overall probability that an

individual is punished conditional upon offending. In this

section, in referring to the certainty of punishment, we are

focusing more specically on the probability that a pun-

ishment is handed out conditional upon arrest. This refers

to the severity of punishment along the extensive margin.

deterrence effect operationalized along the

intensive margin.

4.1 Sentencing

One of the most basic tests of the Becker

model of crime concerns the responsive-

ness of crime to the harshness of criminal

sanctions. Over the past few decades, a lit-

erature has arisen to document the sensitiv-

ity of crime to various sentencing schemes,

sentence enhancements, clemency policies,

“three strikes” laws, and other legislative

actions that change the expected cost of a

criminal sanction. A corresponding literature

measures the responsiveness of crime to the

size of the prison population. With respect

to identication, two challenges are particu-

larly pressing. First, it is difcult to discern

the effect of sentencing policies (which, in

the United States, are generally enacted at

the state level) from other crime reduction

interventions, as well as time-varying factors

that inform the supply of crime more gen-

erally. Attempts to isolate the causal effects

of a change in state-level sentencing policy

invariably encounter the inevitably difcult

issues of choosing an appropriate compari-

son group and selecting from among many

competing and equally plausible models of

aggregate offending. Durlauf and Nagin

(2011) refer to the latter of these issues as

the problem of ad hoc model specication,

referring specically to the under-theorized

manner in which individual-level mental

processes are modeled and the arbitrary

choice of control variables in regressions.

Second, just as prison populations may

affect crime, crime may have a reciprocal

effect on prison populations, creating the

potential for simultaneity bias.

With respect

to identifying deterrence, the chief difculty

is that harsher sanctions may lead to deter-

rence, but typically also to incapacitation.

For a comprehensive review of identication issues in

this literature, see Durlauf and Nagin (2011).

Chaln and McCrary: Criminal Deterrence: A Review of the Literature

This section reviews the literature that seeks

to understand the relationship between sanc-

tions and offending with a particular interest

in discerning the effect that sanctions have

on deterrence.

4.1.1 Prison Populations and Crime

While identifying the elasticity of crime

with respect to a sanction, in principle,

requires an exogenous shock to the sanctions

regime, a natural starting point in unraveling

the crime–sanctions relationship is to con-

sider the elasticity of crime with respect to

the size of the prison population. Studies of

the crime–prison population elasticity gener-

ally utilize state-level panel data and regress

the growth rate in crime on the rst lag of

the growth rate in a state’s share of prisoners.

Marvell and Moody (1994) provide the rst

credible empirical investigation of the elas-

ticity of crime with respect to prison popu-

lations, estimating an elasticity of −0.16. As

in their police paper, they use the concept of

Granger causality in an attempt to rule out a

causal relationship that runs from crime to

prison populations. As discussed in section2,

the approach, while useful, does not offer

compelling reasons to believe in the ignora-

bility of selection bias.

A genuine breakthrough in this literature

is found in Levitt (1996) who, using similar

data, exploits exogenous variation in state

incarceration rates induced by court orders

to reduce prison populations. The intuition

behind the approach is that the timing of

discrete reductions in a state’s prison popula-

tion owing to a court order should be as good

as random. This may not be strictly true, as

the necessity of court orders to reduce over-

crowding may itself be a function of rising

crime rates. However, the strategy relies more

specically on the randomness of the precise

timing of the order and, in our judgment,

represents a plausible strategy for identify-

ing a causal estimate of the effect of prison

populations on crime. Levitt’s estimated

elasticities are considerably larger than those

in Marvell and Moody: −0.4 for violent

crimes and −0.3 for property crimes, while

the largest elasticity reported is for robbery

(−0.7).

An alternative identication strat-

egy can be found in Johnson and Raphael

(2012), who develop an instrumental vari-

able to predict future changes in incarcera-

tion rates. The instrument is constructed by

computing a theoretically predicted dynamic

adjustment path of the aggregate incarcer-

ation rate in response to a given shock to

prison entrance and exit transition proba-

bilities. Given that incarceration rates adjust

to permanent changes in behavior with a

dynamic lag, the authors identify variation in

incarceration that is not due to contempora-

neous criminal offending. Using state-level

panel data covering 1978–2004, Johnson

and Raphael (2012) estimate the elasticity

of crime with respect to prison populations

of approximately −0.1 for violent crimes and

−0.2 for property crimes. Notably, the esti-

mated elasticities in Johnson and Raphael for

earlier time periods were considerably larger

and closer in magnitude to those estimated

by Levitt (1996). Johnson and Raphael con-

clude that the criminal productivity of the

marginal offender has changed considerably

over time as incarceration rates have risen, a

conclusion that is echoed by Liedka, Piehl,

and Useem (2006). With respect to juveniles,

Levitt (1998) studies the response of juvenile

crime to the punitiveness of state-level juve-

nile sentencing along the extensive margin

(the number of juveniles in custody per

capita), concluding that changes in juvenile

sentencing explain approximately 60 per-

cent of the growth in juvenile crime during

the 1970s and 1980s. Using Levitt’s results,

Lee and McCrary (forthcoming) compute an

implied elasticity for violent crimes of −0.4.

Levitt’s analysis is replicated by Spelman (2000) who

reports qualitatively similar ndings.

Journal of Economic Literature, Vol. LV (March 2017)

In sum, estimates of the elasticity of crime

with respect to prison are generally modest

in comparison to police elasticities and fall

between −0.1 and −0.7. The most recent

estimates fall in the low end of that range.

Estimates for violent and property crimes are

of approximately equal magnitude and there

is evidence that the elasticity has diminished

considerably over time as prison populations

have grown. Our best guess is that the current

elasticity of crime with respect to prison pop-

ulations is approximately −0.2, as reported

by Johnson and Raphael (2012).

This nd-

ing is further bolstered by a recent evalua-

tion of “realignment,” a policy implemented

in California to reduce prison overcrowding

by sending additional inmates to county jails,

where they tend to serve shorter sentences.

Lofstrom and Raphael (2013) report that,

with the exception of motor vehicle theft,

there is no evidence of an increase in crime

despite the fact that 18,000 offenders who

would have been incarcerated are on the

street due to the realignment policy. The

magnitude of this elasticity leaves open the

possibility for nontrivial deterrence effects

of prison but, given that prison generates

sizable incapacitation effects, the magnitude

of deterrence is likely small.

4.1.2 Shocks to the Sanctions Regime

A related literature considers the effect of

a discrete change in a jurisdiction’s sanctions

regime that is plausibly not a function of

crime trends more generally. The general

approach is to utilize a differences-in-dif-

ferences design to compare the time-path of

crimes covered by a sentence enhancement

to that of uncovered crimes. The earliest lit-

erature (Loftin and McDowall 1981; Loftin,

Heumann, and McDowall 1983; Loftin and

McDowall 1984; and McDowall, Loftin,

A 2009 review of the literature by Donohue reaches

a similar conclusion.

and Wiersma 1992) considered the effects

of sentence enhancements for specic

crimes—particularly gun crimes—generally

nding little evidence in favor of deterrence.

A more recent paper studies the impact of

changes in sentencing in the aftermath of

London’s 2011 riots. Leveraging the fact that

judges in the United Kingdom handed down

harsher sentences for “riot offenses” in the

six months following the riots, Bell, Jaitman,

and Machin (2014) nd evidence of sizable

declines in riot offenses relative to nonriot

offenses which, in the absence of identiable

changes in policing, they attribute to the

advent of a harsher sanctions regime. This

claim is bolstered by the fact that there was

a relative decline in riot offenses in sectors

that experienced the brunt of the 2011 riots,

as well as sectors that saw no riot activity.

A second class of studies has examined the

impact of changes in the sanctions regime

that have heterogeneous impacts on differ-

ent groups of offenders. For example, Drago,

Galbiati, and Vertova (2009) study the effect

of a 2006 collective clemency of incarcer-

ated prisoners in Italy. Prisoners incarcer-

ated prior to May 2006 were released from

prison with the remainder of their sentences

suspended, while prisoners incarcerated

after May 2006 were ineligible for the clem-

ency. Released prisoners, however, were

subject to a sentence enhancement for any

future crimes committed that were serious

enough to merit a sentence of at least two

years. For such crimes, the sentence would

be augmented by adding the amount of time

the prisoner was sentenced to serve prior to

his pardon to his new sentence. Thus, the

intervention created a situation in which

otherwise similar individuals convicted of

the same crime faced dramatically different

sanctions regimes. The results of this natural

Sentencing did change along both the intensive and

extensive margins, indicating the incapacitation cannot be

ruled out.

Chaln and McCrary: Criminal Deterrence: A Review of the Literature

experiment suggest an elasticity of crime

with respect to sentence length of approxi-

mately −0.5 at one year follow-up. Utilizing

the same natural experiment, Buonanno and

Raphael (2013) report evidence that inca-

pacitation effects forgone as a result of the

collective clemency were large, thus con-

straining the magnitude of the deterrence

effect.

Similar ndings are reported for the

United States by Helland and Tabarrok

(2007). Using data from California’s three-

strikes regime, Helland and Tabarrok (2007)

compare the criminal behavior of individuals

convicted of a second “strikeable” offense to

those tried for a second strikeable offense

but who were ultimately convicted of a lesser

offense. As Durlauf and Nagin (2011) note,

individuals with one strike may not be an

ideal comparison group for a variety of rea-

sons—in particular, it may be the case that

the individuals with two strikes had poorer

legal representation or that the precise nature

of their potential second-strike offense was

qualitatively less serious. Nevertheless, the

authors demonstrate that comparing two-

strike to one-strike individuals is sufcient

to remove a great deal of the selection bias

that exists in comparing individuals with

two strikes to the remainder of the charged

population. The authors nd evidence of

an appreciable deterrent effect, calculat-

ing that California’s three-strikes legislation

reduced felony arrest rates by approximately

20 percent among criminals with two strike-

able offenses against them. Similarly, while

Zimring, Hawkins, and Kamin (2001) nd

little evidence of an overall effect of three-

strikes legislation, they do nd evidence that

individual offenders with two strikes are less

likely to be arrested. Given that the deter-

rence margin is most salient at two strikes,

these studies stand out as especially import-

ant with respect to identifying a meaning-

ful deterrence effect of sentencing. On the

other hand, the magnitude of the response

is actually quite small once one considers

the increase in sentence lengths associated

with three strikes. Helland and Tabarrok’s

estimates suggest an elasticity of crime with

respect to sentence length of −0.06.

Last but not least, we survey a completely

different idea with respect to changing the

sanctions regime. While sentence enhance-

ments and three-strikes laws are designed

specically to increase sanction severity

across either the intensive or the extensive

margin or both, it is possible to imagine

simultaneously making one margin harsher

and the other one less harsh. This is the

premise underlying swift-and-certain sanc-

tions regimes (Hawken and Kleiman 2009

and Kleiman 2009). The idea of swift-and-

certain sanctions arises from the notion that

myopic individuals are unlikely to be respon-

sive to long sentences but may be highly

responsive to short sentences if they are

issued with near certainty. In recent prac-

tice, Hawaii’s Opportunity Probation with

Enforcement (HOPE) program is the canon-

ical example of swift-and-certain sanctioning

in action. In an effort to address chronic

recidivism among probationers, Hawaii First

Circuit Court Judge Steven Alm recognized

that punishments for violating the terms of

probation were fairly unlikely and, if meted

out, tended to occur in the distant future.

Moreover, the sanctions were typically

harsh and, as such, costly. Judge Alm and

his collaborators put into practice a program

that addressed probation violations with

immediate but light sanctions—typically

ranging from warnings to spending up to a

week in jail. Probationers were intensively

monitored, with any violations resulting in

a sanction. In a banner nding, Hawken

and Kleiman (2009) nd that individuals

assigned at random to HOPE, as opposed to

business as usual, were 55 percent less likely

to be arrested for a new crime, 72 percent

less likely to use drugs, and 53 percent less

likely to have their probation revoked than

Journal of Economic Literature, Vol. LV (March 2017)

those on regular probation. In a similarly

promising related study, Kilmer et al. (2013)

found that a swift-and-certain program in

South Dakota targeted towards persistent

alcohol-involved offenders appears to have

had extraordinarily large effects in counties

that received the program.

4.2 Capital Punishment Regimes

Variation in the presence or intensity of

capital punishment generates a potentially

excellent source of variation with which

to test for the magnitude of general deter-

rence. In particular, to the extent that vari-

ation in a state’s capital-punishment regime

is unrelated to changes in the intensity of

policing, the effect of capital punishment

represents a pure measure of deterrence

with any response of murder to the presence

or intensity of capital punishment not plausi-

bly attributable to incapacitation.

There have been two primary approaches

to identifying deterrence effects of capital

punishment. One approach considers the

use of granular time-series data or event

studies to identify the effect of the timing of

executions on murder. Time-series studies

typically use vector autoregression to assess

whether murder rates appear to decline in

the immediate aftermath of an execution.

Prominent examples include Stolzenberg and

D’Alessio (2004), which nds no evidence

of deterrence, and Land, Teske, and Zheng

(2009), which nds evidence of short-run

deterrence. Event studies such as those of

Grogger (1991) and Hjalmarsson (2009a)

examine the daily incidence of homicides

before and after executions. Both Grogger

(1991) and Hjalmarsson (2009a) nd little

evidence of deterrence effects though, as

The argument is that in the absence of a capital pun-

ishment regime or a death sentence, a convicted offender

would nevertheless be sentenced to a lengthy prison sen-

tence (such as a life sentence) without the possibility of

parole.

Charles and Durlauf (2013) and Hjalmarsson

(2012) note, with a limited time horizon, it is

not possible to distinguish between what we

typically think of as deterrence and tempo-

ral displacement. A related study, Cochran,

Chamlin, and Seth (1994), considers the

effect of Oklahoma’s rst execution in more

than twenty years and nds evidence that the

execution appears to have increased murder

among strangers, an effect they attribute

to a “brutalization” hypothesis, though it is

attributed with equal ease to statistical noise.

A nal study worth noting is that of Zimring,

Fagan, and Johnson (2010), who compare

homicide rates between Singapore, which

uses the death penalty with variable inten-

sity, and Hong Kong, which does not use the

death penalty. The paper nds no evidence

in favor of deterrence, as both countries

experience similar homicide trends over the

thirty-ve-year time period studied.

Broadly speaking, the time-series and

event-studies literatures offer little support

in favor of deterrence though, as noted by

Charles and Durlauf (2013), the literature is

plagued by several conceptual problems that

compromise the interpretability of estimated

treatment effects. In particular, the focus of

the time-series literature on executions, as

opposed to the sanctions regime more gener-

ally, marks a divergence from the neoclassi-

cal model of crime insofar as the occurrence

of an execution does not per se change the

expected severity of a criminal sanction for

murder.

Indeed the research design is

often motivated by the assumption that an

execution affects an offender’s perceived

An important exception to this general point can be

found in Chen (2013), which studies the effect of execu-

tions for desertion among British soldiers during World

War I and nds evidence that executions deter desertion,

but actually encourage desertion when the execution was

for an offense other than desertion or if the executed sol-

dier was Irish. The reason this study stands as an exception

to the rule proposed by Charles and Durlauf is that during

a time of war, the sanction regime is likely to be in constant

ux.

Chaln and McCrary: Criminal Deterrence: A Review of the Literature

sanction. However, there is little evidence,

empirical or otherwise, to support this

assumption. Second, Charles and Durlauf

note that the underlying logic of time-series

analyses of executions and murder opera-

tionalize as deterrence the dynamic correla-

tions between a shock to one time series and

the levels of another. As the authors note,

this is an arbitrary conceptualization of what

is meant by deterrence.

A second literature studies the deterrent

effect of capital punishment utilizing panel

data on US states to identify the effect of

a capital-punishment statute or the fre-

quency of executions on murder among the

public at large. In particular, these studies

have exploited the fact that in addition to

cross-state differences in sentencing policy,

there is also variation over time for individ-

ual states in the ofcial sentencing regime,

the propensity to seek the death penalty in

practice, and the application of the ultimate

punishment (Chaln, Haviland, and Raphael

2013). This literature has generated mixed

ndings with several prominent papers (e.g.,

Dezhbakhsh, Rubin, and Shepherd 2003;

Mocan and Gittings 2003; Zimmerman

2004, 2006; and Dezhbakhsh and Shepherd

2006) nding large and signicant deter-

rence effects, and several equally promi-

nent papers (Katz, Levitt, and Shustorovich

2003; Berk 2005; Donohue and Wolfers

2005, 2009; and Kovandzic, Vieraitis, and

Paquette-Boots 2009) nding little evidence

in favor of deterrence.

While evidence in favor of deterrence

is mixed, recent reviews by Donohue and

Wolfers (2005, 2009) and Chaln, Haviland,

and Raphael (2013), as well as a 2011 report

commissioned by the National Academy of

Sciences, point to substantial problems in a

The debate continues with recent responses to

critiques by Donohue and Wolfers (2005) offered by

Zimmerman (2009), Dezhbakhsh and Rubin (2011), and

Mocan and Gittings (2010).

number of papers that purport to nd deter-

rence effects of capital punishment. These

problems include the use of weak and/or

inappropriate instruments (Dezhbakhsh,

Rubin, and Shepherd 2003; and Zimmerman

2004), failure to report standard errors that

are robust to within-state dependence

(Dezhbakhsh and Shepherd 2006 and

Zimmerman 2009), and sensitivity of esti-

mates to different conceptions of perceived

execution risk (Mocan and Gittings 2003).

More generally, the panel-data literature

suffers from the threat of policy endogene-

ity, failure to include accurate controls, and

a lack of knowledge regarding how potential

offenders perceive execution risk. Finally,

as noted by Berk (2005) and Donohue and

Wolfers (2005), results are highly sensitive

to the inclusion of certain states and even

certain inuential data points (i.e., Texas in

1997). The most careful paper to date is that

of Kovandzic, Vieraitis, and Paquette-Boots

(2009), who use a dataset spanning a longer

period of time, employ an expanded set of

control variables, and explore a wide variety

of operationalizations of the effect of capital

punishment and execution risk. The authors

nd no evidence of a deterrent effect.

4.3 Sanction Nonlinearities

An additional literature that seeks to esti-

mate the magnitude of deterrence effects

does so by exploiting nonlinearities in the

severity of sanctions faced by certain offend-

ers. Typically, these studies estimate the inci-

dence of arrest rates for young offenders who

are either just below or just above the age

of criminal majority—generally either sev-

enteen or eighteen years of age, depending

While Mocan and Gittings (2010) provide an extensive

summary of the robustness of results reported in Mocan

and Gittings (2003), Chaln, Haviland, and Raphael (2013)

point out that the responsiveness of murder to execution

risk relies on the assumption that individuals are executed

fairly soon (within six years) of a conviction.

Journal of Economic Literature, Vol. LV (March 2017)

on the state. While offenders below a given

state’s age cutoff are adjudicated as juveniles

and face relatively low sanctions risk, offend-

ers who are just above the age of majority

are adjudicated as adults and are subject to

considerably more severe sanctions. Given

that the conditional probability of an arrest is

smooth as a function of age around the age of

criminal majority, any behavioral response of

offenders to the threshold is assumed to be

the result of deterrence.

The canonical paper in this literature is

that of Lee and McCrary (forthcoming).

Using data from Florida, Lee and McCrary

document a sizable discontinuity in the prob-

ability that a young offender is sentenced to

prison depending upon whether the arrest

occurred prior to or after the offender’s

eighteenth birthday. Despite the fact that

the expected sentence length for an adult

arrestee is over twice as great as that faced

by a juvenile offender, Lee and McCrary nd

little evidence of deterrence. Their estimates

suggest an elasticity of crime with respect to

sentence lengths of approximately −0.05,

an estimate that is far smaller than that of

Drago, Galbiati, and Vertova (2009), who

estimated an elasticity for Italian adults.

Findings in Lee and McCrary are perhaps

surprising, but are supported by results

reported in Hjalmarsson (2009b), who docu-

ments that perceived increases in the sever-

ity of sanctions at the age of criminal majority

among juvenile offenders are smaller than

the actual changes, thus suggesting a mech-

anism underlying these small effects. The

implication is that deterrence is not opera-

tional because perceptions do not match the

incentives created by public policy.

Lee and McCrary’s research design has

now been replicated to varying degrees.

Most recently, Hansen and Waddell (2014)

study the effect of Oregon’s age of major-

ity on juvenile offending and report some

evidence of a decline in crime upon reach-

ing the age of majority for covered crimes.

However, results that utilize an appropriately

small bandwidth are not signicant at con-

ventional levels indicating, at best, weak evi-

dence in favor of deterrence effects. Finally,

in a reduced-form analysis using national-

level data in the NLSY, Hjalmarsson (2009b)

nds little evidence of deterrence around

state-specic ages of majority using self-re-

ported data on offending.

A nal paper worth mentioning is that

of Hjalmarsson (2009b), which studies the

effect of serving time in prison on subse-

quent arrest among juvenile offenders in

Washington State. Exploiting a disconti-

nuity in the state’s sentencing guidelines,

Hjalmarsson reports that incarcerated juve-

niles have lower propensities to be recon-

victed of a crime. This deterrent effect is also

observed for older and more criminally expe-

rienced offenders. The differential ndings

in Hjalmarsson (2008), on the one hand, and

Hjalmarsson (2009b) and Lee and McCrary

(forthcoming), on the other hand can poten-

tially be rationalized by the fact that while

the latter studies considered the behavioral

response to a general threat of punishment,

the former study measures the behavioral

response to actual punishment that has

already been experienced.

On the whole, the RD literature around

the age of criminal majority produces little

evidence of deterrence among young offend-

ers. The available evidence suggests that this

may, in part, be due to a lack of awareness of

the size of the sanctions discontinuity, leav-

ing open the possibility that deterrence may

be found if the discontinuity is “advertised”

as in pulling-levers-type focused-deterrence

strategies. A remaining issue concerns the

focus of the literature on arrests, which are

an imperfect proxy for offending. In partic-

ular, if police ofcers are less likely to arrest

an individual just below the age of majority

relative to an individual just above the age

of majority for a given crime, the resulting

RD estimates will be attenuated with respect

Chaln and McCrary: Criminal Deterrence: A Review of the Literature

to the actual change in offending, which may

well have been positive. While no direct

evidence suggests that this type of ofcer

behavior is widely employed, the concern is

worth noting.

4.4 Deterrence versus Incapacitation

As with the literature examining the

response of crime to the certainty of appre-

hension, the primary conceptual challenge to

interpreting the empirical literature on sanc-

tions is that it is difcult to discern between

deterrence and incapacitation. With respect

to studies of the crime–prison population

elasticity, two issues merit discussion. First,

the size of a state’s prison population is only

a proxy for the punitiveness of the sanctions

regime. In practice, the size of the prison

population is a function of many things: the

underlying rate of offending, the certainty of

punishment (in part due to the probability of

apprehension), and the criminal propensity of

the marginal offender when the prison popu-

lation changes. Prison population is a stock,

not a ow, and accordingly when the prison

population declines it can be due to either

an increase in the contemporary probabil-

ity of a custodial sentence or to ows out of

prison (Durlauf and Nagin 2011). Likewise,

deterrence is only one of the mechanisms by

which prisons affect crime, the other being

incapacitation. For these reasons, the litera-

ture that examines the crime–prison popula-

tion elasticity, while important with respect

to public policy, is not particularly informa-

tive with respect to deterrence.

In our view, research that studies the

instantaneous impact of shocks to the sanc-

tions regime are considerably more infor-

mative. Indeed, identifying the sensitivity of

crime to a shock to the sanctions regime is

conceptually close to testing Becker’s pre-

diction that behavior will respond to the

severity of a sanction. However, even with

perfect identication, attributing a change

in offending that occurs in the aftermath of

a sanctions shock to deterrence requires a

logical leap. In particular, the logical leap is

greatest when the sanctions regime becomes

more punitive along both the intensive and

extensive margin. To the extent that a cus-

todial sentence becomes both longer and

more likely, tougher sentencing generates

both deterrence and incapacitation effects.

This is an issue in interpreting much of the

literature on sentence enhancements. Such

a concern is addressed in Kessler and Levitt

(1999), which studies the effect of California

Proposition 8, a 1982 ballot amendment that

enhanced the length of sentences for certain

felonies, but not for others. Because prior to

Proposition 8, each of the felonies already

required mandatory prison time, any instan-

taneous response of crime to Proposition 8

would have to be attributable to deterrence.

Kessler and Levitt nd that crimes that

were eligible for the enhancement fell by

between 4 and 8 percent in the aftermath of

Proposition 8, relative to a control group of

crimes not eligible for the enhancement. The

implication of these ndings is that increased

sanctions promote substantial deterrence.

However, while the logic is, in general, per-

suasive, the validity of Kessler and Levitt’s

results have been called into question by

Webster, Doob, and Zimring (2006), who

argue that overall crime did not fall in the

aftermath of Proposition 8, and by Raphael

(2006), who argues that crimes ineligible

for sentence enhancements do not form an

appropriate control group for crimes eligible

for the enhancement.

Changes in a state’s use of capital pun-

ishment, in theory, offers a more appropri-

ate means of identifying deterrence. This is

because capital murder is sufciently serious

as to warrant a long prison sentence regard-

less of the specics of a state’s sentencing

regime. Hence, when an offender is sen-

tenced to death (as opposed to a sentence of

life without the possibility of parole), there is

no instantaneous incapacitation effect. With

Journal of Economic Literature, Vol. LV (March 2017)

respect to capital punishment, the evidence

of deterrence is, at best, mixed with the most

rigorous studies failing to nd evidence of

deterrence. Moreover, the identication

problems in the literature are considerable,

as it is difcult to identify a shock to a state’s

capital-punishment regime that is plausibly

exogenous. Overall, we do not believe this

literature offers any credible evidence of

deterrence, though it is not clear that varia-

tion in capital-punishment regimes will ever

be sufciently random and that murder rates

will ever be sufciently dense to allow us to

credibly detect a treatment effect.

Undoubtedly the best tests for deterrence

may be found in research that follows individ-

ual offenders who, upon being apprehended,

face different sanctions for a given crime.

To the extent that differential treatment is

driven by arbitrary distinctions within the

criminal-justice system, research can iden-

tify deterrence by comparing the behavior of

offenders who are otherwise similar but are

treated differently. Such a research design

is truly quasi-experimental in the sense that

treatment effects can be interpreted using

the language of the Rubin causal model.

Moreover, individual-level studies track

the behavior of individuals who are not in

prison and accordingly are not incapacitated.

Hence, any behavioral shift is plausibly attrib-

utable to deterrence. These individual-level

studies produce mixed evidence. On the one

hand, studies of three-strikes laws establish

that offenders with two strikes are less likely

to reoffend than offenders with one strike

(Zimring, Hawkins, and Kamin 2001 and

Helland and Tabarrok 2007). Likewise, in

Drago, Galbiati, and Vertova’s study of Italy’s

clemency bill, prisoners who faced harsher

sanctions upon being rearrested were less

likely to be rearrested. On the other hand,

studies of sanction nonlinearities in which

offenders of slightly different ages receive

differential treatment report little evidence

of a large deterrence effect. Of course, these

results might be rationalized by differences

in the responsiveness to a sanction among

offenders of different ages.

To date, the degree to which offenders

are deterred by harsher sanctions remains

an open question. Undoubtedly, deterrence

can exist in extreme circumstances in which

the punishment is immediate and harsh.

Likewise, evidence of deterrence is found

when punishment severity faced by individ-

ual offenders is both extraordinarily severe

and known. However, within the range of

typical changes to sanctions in contempo-

rary criminal-justice systems, the evidence

suggests that the magnitude of deterrence

owing to more severe sentencing is not large

and is likely to be smaller than the magnitude

of deterrence induced by changes in the cer-

tainty of capture. What is less well understood

is the extent to which changing sentencing

severity along the extensive margin induces

deterrence. Since this increases the severity

of punishment in the near rather than the

distant future, one might think that deter-

rence effects will be more easily observed.

5. Work and Crime

The nal pillar of the neoclassical model of

crime considers the responsiveness of crime

to a carrot (better employment opportuni-

ties) rather than a stick (certainty or sever-

ity of punishment). In particular, since the

benet of a criminal act must be weighed

against the value of the offender’s time

spent in an alternative activity, an increase

in the opportunity cost of an offender’s time

can be thought of as a deterrent to crime.

Indeed, this principle has generated consid-

erable public support for a variety of policies

designed to reduce recidivism among offend-

ers returning from prison—for example, the

provision of job training, employment coun-

seling, and transitional jobs.

The empirical literature examining the

impact of local labor-market conditions on

Chaln and McCrary: Criminal Deterrence: A Review of the Literature

crime can be divided into two related but dis-

tinct research literatures. The rst literature

examines the relationship between unem-

ployment and crime. A second literature

examines the impact of the responsiveness

of crime to wages.

With respect to both

literatures, approaches to study the effect

of labor markets on crime are varied and

include papers that use individual micro-

data, as well as state- or county-level vari-

ation. Taken as a whole, the literature that

uses aggregate data to disentangle the effect

of economic conditions on crime presents a

mixed picture. In general, results are sensi-

tive to the time period studied, the popula-

tion under consideration, the type of wage or

unemployment rate that is employed, as well

as the criminal offenses analyzed. However,

more recent and carefully identied papers

tend to nd evidence of a fairly robust rela-

tionship between both unemployment and

wages and crime. There is also a literature

that examines the relationship between

crime, unemployment, and wages using indi-

vidual data. We discuss the implications of

this literature for the study of deterrence in

the nal part of this section.

5.1 Unemployment

Periods of unemployment are thought to

generate incentives to engage in criminal

activity, either as a means of income sup-

plementation or consumption smoothing or,

more generally, due to the effect of psycho-

logical strain (Chaln and Raphael 2011). To

the extent that a decline in unemployment

raises the opportunity cost of crime with-

out generating a subsequent increase in the

probability of apprehension or the severity of

the expected sanction, the response of crime

There is also a large and growing experimental litera-

ture that evaluates how at-risk individuals have responded

to the provision of job coaching, employment counseling,

career placement, and other employment-based services.

to changes in the unemployment rate can

be thought of as capturing, among a host of

behavioral responses, deterrence.

In general, the early literature linking

unemployment and crime has produced

mixed and frequently contradictory results,

leading Chiricos (1987) to characterize schol-

arly opinion on the topic as a “ consensus of

doubt.” In particular, Chiricos found that,

among the studies he reviewed, fewer than

half found signicant positive effects of

aggregate unemployment rates on crime

rates.

This conclusion is echoed in reviews

by Freeman (1983), Piehl (1998), Mustard

(2010), and Chaln and Raphael (2011).

Recent literature on the topic of unem-

ployment and crime has beneted from

several methodological advances—in partic-

ular, the use of panel data as opposed to a

cross-sectional data or national time series.

Examples of panel-data research include

Entorf and Spengler (2000) for Germany,

Papps and Winkelmann (2000) for New

Zealand, Machin and Meghir (2004) for

the United Kingdom, Andresen (2013) for

Canada, and Arvanites and Dena (2006),

Ihlanfeldt (2007), Rosenfeld and Fornango

(2007), and Phillips and Land (2012) for the

United States. With the exception of Papps

and Winkelmann (2000), each of these

papers nds at least some evidence in favor

of a link between unemployment and crime,

in particular, property crime.

Nonetheless, Chiricos’s review also found that the

unemployment–crime relationship was three times more

likely to be positive than negative and fteen times more

likely to be positive and signicant than negative and sig-

nicant, indicating a basis for further research. The results

were especially strong for property crimes—in particu-

lar, larceny and burglary. Chiricos suggests that research

results are generally consistent by level of aggregation,

though they tend to be more consistently positive and sig-

nicant at lower levels of aggregation. This hypothesis is

echoed by Levitt (2001), who likewise argues that national-

level time-series analyses obscure the unemployment–

crime relationship by failing to account for rich variation

across space.

Journal of Economic Literature, Vol. LV (March 2017)

A second innovation in the recent litera-

ture has been to employ instrumental vari-

ables to address the potential endogeneity

between labor-market conditions and crime.

The rst such study is that of Raphael and

Winter-Ebmer (2001), who use a state-level

panel data set covering 1979–98 to study

the effect of unemployment rates on various

types of crime employing two instruments for

the unemployment rate—the value of mili-

tary contracts with the federal government,

as well as the regional impact of shocks to

the price of oil. For property-crime rates, the

results consistently indicate a positive effect

of unemployment on crime with a 1 percent-

age point increase in the unemployment rate

predicting a 3–5 percent increase in prop-

erty crime. For violent crime, however, the

results are mixed. Gould, Weinberg, and

Mustard (2002) provide a similar analysis

at the county level, using a county’s initial

industry mix and measures of skill-biased

technical change as an instrument for unem-

ployment. They too nd evidence of a pos-

itive relationship between unemployment

and crime, particularly property crime. Taken

as a whole, results reported in Raphael and

Winter-Ebmer (2001) and Gould, Weinberg,

and Mustard (2002) imply that variation in

unemployment rates explained between

12percent and 40 percent of the decline in

property crime during parts of the 1990s. In

a more recent paper, Lin (2008) builds on

these approaches using exchange-rate shocks

to isolate exogenous variation in unemploy-

ment rates. Lin reports that a 1 percentage

point increase in unemployment leads to a

4 to 6 percent decline in property crime and

would explain roughly one-third of the crime

drop during the 1990s.

On the whole, the preponderance of the

evidence suggests that there is an important

Fougere, Kramarz, and Pouget (2009) provide a sim-

ilar analysis for France, nding effects that are similar in

magnitude.

relationship between unemployment rates

and property crime, but little impact of

unemployment on violent crime, a con-

clusion echoed in a recent review by Cook

(2010). In the recent literature, which is

more careful with respect to addressing

omitted variables bias and simultaneity, the

relationship between unemployment and

property crime is found regardless of the

level of aggregation (counties or states).

The relationship between unemployment

and property crime is empirically meaning-

ful, as property crime would be predicted to

rise by between 9 and 18 percent during a

serious recession in which unemployment

increased by 3 percentage points. Moreover,

this, if anything, may understate the magni-

tude of the relationship, as crime appears to

be particularly sensitive to the existence of

employment opportunities for low-skilled

men (Schnepel 2013). Nevertheless, the

estimates remain sensitive to the time period

studied. To wit, property crime has gener-

ally continued to decline through the recent

Great Recession, which increased unem-

ployment rates nationally by as many as

4percentage points.

5.2 Wages

A second and related research literature

considers the impact of wage levels on crime

rates. There are several a priori reasons to

expect a stronger relationship between

Prominent IV papers, including Raphael and

Winter-Ebmer (2001); Gould, Weinberg, and Mustard

(2002); and Lin (2008) do not uniformly nd that instru-

menting results in a more positive relationship between

unemployment and crime as would be predicted by the

omission of procyclical control variables or simultane-

ity bias. Another explanation for slippage between least

squares and IV estimates of the effect of unemployment

on crime is measurement errors in the unemployment rate.

To the extent that such errors are classical, attenuation bias

will mean that 2SLS estimates will exceed ordinary least

squares (OLS) estimates. To the extent that this pattern

is not found, there is the possibility that OLS estimates

are actually upward biased due to simultaneity or omitted

variables.

Chaln and McCrary: Criminal Deterrence: A Review of the Literature

crime and wages than between crime and

unemployment. First, as noted by Gould,

Weinberg, and Mustard (2002), since crim-

inal participation is associated with a set of

xed costs, crime may well be more respon-

sive to long-term labor-market measures

such as levels of human capital or wages than

unemployment spells, which are typically

ephemeral. Second, at any given time, the

number of individuals who are employed in

low-wage jobs vastly outnumbers the num-

ber of unemployed and, as such, wages for

unskilled men may play a proportionally

greater role than unemployment in encour-

aging crime (Hansen and Machin 2002). In

fact, among individuals who reported engag-

ing in crime during the past year, a large

majority reported wage earnings (Grogger

1998) and three-quarters were employed at

the time of their arrest, indicating that the

behavior of a majority of offenders should be

sensitive to changes in the wage.

The literature linking wages to crime has,

in general, generated more consensus than

the unemployment literature. Prominent

panel-data papers include Doyle, Ahmed,

and Horn (1999), who analyze state-level

panels for 1984–93 and nd that higher aver-

age wages reduce both property and violent

crime (elasticity estimates vary between

−0.3 and −0.9), and Gould, Weinberg, and

Mustard (2002), who restrict their analysis

to the wages of relatively low-skilled men

and nd, using a county-level panel span-

ning 1979 to 1997, that the falling wages

of unskilled men in this period led to an

18percent increase in robbery, a 14 percent

increase in burglary, and a 7 percent increase

in larceny. These ndings are striking in that

they indicate that wage trends explain more

than half of the increase in both violent and

property crimes over the entire period.

In a similar analysis for the United

Kingdom, Machin and Meghir (2004)

examine changes in regional crime rates

in relation to changes in the tenth and

twenty-fth percentiles of the region’s wage

distribution and focus on the retail sector,

an industry where low-skilled workers have

the ability to manipulate their hours of work.

They nd that crime rates are higher in areas

where the bottom of the wage distribution

is low. With regard to microdata, Grogger

(1998), leveraging data from the NLSY, nds

that youth wages account for approximately

three-quarters of the variation in youth

crime. Finally, a related literature consid-

ers the responsiveness of crime to minimum

wages and consistently nds evidence in

favor of a negative relationship between the

two variables (Corman and Mocan 2005 and

Fernandez, Holman, and Pepper 2014).

5.3 Individual-Level Studies

In addition to aggregate-level studies

that examine the impact of macroeconomic

uctuations on crime, there is a parallel

literature that studies the effect of wage

shifts and job loss—mainly job loss—over

the life course. This literature uses natu-

ral variation to examine whether offending

rises when individuals nd themselves out

of work. An early example of such research

is that of Crutcheld and Pitchford (1997),

who, using data from the 1979 NLSY79,

nd that the approximately 8,000 adults

who responded to the rst wave of the sur-

vey were more likely to engage in crime

when they are out of the labor force and

when they expect their current job to be of

short duration. A host of similar cohort stud-

ies have found similar correlations among

males in London, as well as individuals born

in Philadelphia in 1945 (Thornberry and

Christenson 1984 and Witte and Tauchen

1993). For several reasons, we do not believe

this literature has great value in uncovering

deterrence effects. First, even conditioning

on xed effects, individual-level models do

not plausibly account for omitted variation

that may be related to both unemployment

and offending. In particular, a change in an

Journal of Economic Literature, Vol. LV (March 2017)

important unobserved factor may drive both

spells out of work and criminal activity. For

example, illegal drug use may simultane-

ously cause both an unemployment spell as

well as participation in crime. Alternatively,

other life stresses, problems with personal

relationships, mental health problems, etc.

may cause the simultaneous co-occurrence

of unemployment (or underemployment)

and criminal activity (Chaln and Raphael

2011). To be sure, such issues of causal iden-

tication pose a challenge in all micro-level

social science research using observational

data. Nonetheless, absent a clear source of

exogenous variation in employment status or

employment prospects, one should probably

consider these sorts of longitudinal estimates

as providing an upper bound on the likely

effect sizes.

A second individual-level literature is, in

our view, more useful. This literature con-

siders the impact of providing employment

services, or, in some cases, transitional jobs

to former prisoners—in particular assessing

whether such programs reduce recidivism.

The research is predominantly comprised

of randomized experiments and is, as such,

highly credible. Experimental interventions

of this nature tend to include programs that

provide income, employment-based ser-

vices, or skills-building social services. There

are over a dozen experimental evaluations of

such efforts in the United States, in which

treatment group members are randomly

assigned. A key advantage of these studies

is that the treatment is clearly exogenous,

and, as such, any observed impacts plausibly

represent true causal effects. However, the

reader should be careful in interpreting the

results of these programmatic interventions,

as it is often the case that many members of

the randomized control group receive sim-

ilar services elsewhere. Often, it is difcult

to document such contamination and it is

not always self-evident that the interven-

tion has a large marginal impact on service

delivery. Second, since these interventions

are targeted at particular groups with offense

histories that cross fairly stringent severity

levels (former prisoners, for example), they

tend to treat individuals who may not be par-

ticularly responsive to positive incentives.

Community-based employment interven-

tions became popular in the United States

beginning in the 1970s. Under authority

of the 1962 Manpower Development and

Training Act, the US Department of Labor

launched a number of programs aimed at

former prisoners beginning with the Living

Insurance for Ex-Prisoners program, which

provided a living stipend and job-placement

assistance to prisoners returning to Balti-

more between 1972 and 1974, and the

Transitional Aid Research Project (TARP),

which provided various combinations of

cash assistance and job-placement services

to ve different experimental groups of

ex-offenders in Georgia and Texas. The ear-

lier demonstration evaluated by Mallar and

Thorton (1978) found signicant impacts of

the income-support program, with consider-

ably lower offending rates among the treat-

ment group. However, the evaluation of the

larger-scale TARP program (Rossi, Berk, and

Lenihan 1980) found little effect. The latter

evaluation also found a large negative effect

of the transitional cash assistance on the

labor supply of released inmates. In fact, the

authors speculate that the lack of an overall

impact on recidivism reected the offsetting

effects of the reduction in recidivism due to

the cash assistance and the increased crimi-

nal activity associated with being idle (Rossi,

Berk, and Lenihan 1980).

There have also been several high-quality

evaluations of the impact of providing tran-

sitional employment to former inmates.

The National Supported Work (NSW) pro-

gram (recently reanalyzed by Uggen 2000)

and the New York Center for Employment

Opportunities currently under evaluation

by the Manpower Development Research

Chaln and McCrary: Criminal Deterrence: A Review of the Literature

Corporation (MDRC) (Bloom et al. 2007)

nd some evidence that providing prison

releases with transitional employment

forestalls recidivism during the two years

post-release. However, these programs

found considerable heterogeneity in pro-

gram impacts with the NSW, nding sig-

nicant effects for older releases and the

CEO evaluation reporting signicant effects

for only those most recently released from

prison. Moreover, the majority of the liter-

ature reports little evidence of an effect of

employment status on recidivism among

reentering prisoners (Visher, Wintereld,

and Coggeshall 2005). It may well be that

employment deters crime among individuals

without prison experience, a mechanism that

plausibly underlies relationships between

measures of macroeconomic performance

and crime. However, among individuals with

serious criminal records, the evidence of

deterrence is difcult to nd.

5.4 Identifying Deterrence

A rst-order issue in interpreting research

on the effect of police and prisons on crime

concerns the extent to which deterrence

can be disentangled from incapacitation.

This issue is not relevant in considering the

responsiveness of crime to changes in wages

or employment conditions. Nevertheless, it is

worth considering whether a signicant coef-

cient on the wage or unemployment rate

in a crime regression necessarily identies

deterrence. In particular, for several reasons,

crime and unemployment may be related

due to factors other than deterrence. First,

a relationship between macroeconomic con-

ditions and crime may exist due to the rela-

tionship between macroeconomic conditions

and criminal opportunities (Cook and Zarkin

1985). For example, during a recession, auto

thefts tend to decline presumably because

fewer people are employed and therefore

drive their cars less frequently (Cook 2010).

Second, employment conditions and crime

may be linked through behavioral changes

that cannot be properly characterized as

deterrence. For example, a displaced worker

may well develop feelings of anger or loss of

control that subsequently manifest in violent

behavior. In such a case, the job may not be

protective against crime through any deter-

rence mechanism per se. Nevertheless, a

robust relationship between economic con-

ditions and crime is potentially consistent

with the idea that individuals respond to

incentives, at least at the margin.

6. Conclusion

We reviewed three large literatures

regarding the responsiveness of crime to

police, sanctions, and local labor-market

opportunities. Three key conclusions are

worth noting. First, there is robust evidence

that crime responds to increases in police

manpower and to many varieties of police

redeployments. With respect to manpower,

our best guess is that the elasticity of vio-

lent crime and property crime with respect

to police are approximately −0.4 and −0.2,

respectively. The degree to which these

effects can be attributed to deterrence as

opposed to incapacitation remains an open

question, though analyses of arrest rates sug-

gests a role for deterrence (Levitt 1998 and

Owens 2013). With respect to deployments,

experimental research on hot-spots policing

and focused deterrence efforts have, in some

cases, led to remarkably large decreases in

offending, a fact that may be attributable to

the visibility of such policies.

Second, while the evidence in favor of a

crime–sanction link generally favors rela-

tively small deterrence effects, there does

appear to be some evidence of more mean-

ingful deterrence induced by policies that

target specic offenders with sentence

enhancements. This is seen in the effect of

California’s three-strikes law on the behav-

ior of offenders with two strikes (see Helland

Journal of Economic Literature, Vol. LV (March 2017)

and Tabarrok 2007) and in the behavior of

pardoned Italian offenders (Drago, Galbiati,

and Vertova 2009). On the other hand,

while the elasticity of crime with respect to

sentence lengths appears to be large in the

Italian case, it is quite small in the California

case.

Finally, there is fairly strong evidence, in

general, of a link between local labor-mar-

ket conditions, proxied using the unem-

ployment rate or the wage, on crime. While

these effects are unlikely to be appreciably

contaminated by incapacitation effects, they

may reect behavioral responses aside from

deterrence. Moreover, it is not clear that sup-

plying employment deters offending among

the most criminally productive individuals.

Overall, the evidence suggests that indi-

viduals respond to the incentives that are the

most immediate and salient. While police

and local labor-market conditions inuence

costs that are borne immediately, the cost

of a prison sentence, if experienced at all, is

experienced sometime in the future. To the

extent that offenders are myopic or have a

high discount rate, deterrence effects will be

less likely. Moreover, given that an empirical

nding of deterrence depends on the exis-

tence of perceptual deterrence, it may be

the case that potential offenders are more

aware of changes in policing and local labor-

market conditions than they are of changes

in incarceration policy, with the exception

of specic sentence enhancements that are

individually targeted. In the nal section of

this article, we return to Becker’s economic

model of crime in an attempt to rationalize

the empirical literature with the theory that

precipitated it. We close with a couple of

concrete recommendations for future work.

6.1 Rationalizing Theory and Empirics

A natural starting place in reconciling the

empirics with the theory is to consider that

Becker’s model does not explicitly predict

that offending will be more responsive to

changes in p as opposed to f . This is because

while the model allows for varying degrees

of risk aversion, there is no other mechanism

within the model that can lead to a differ-

ential response of crime to p and f that we

tend to observe in the empirical literature.

However, using more recent theoretical

work that builds upon Becker as a guide, we

note that there are at least three strong the-

oretically motivated reasons to expect that

crime will be more responsive to p than to

f . This is not to say that the Becker model

is incorrect—on the contrary, our reading

of the empirical literature is that it provides

support for the model. Instead, we prefer to

characterize the model as a useful starting

point for thinking about some of the more

nuanced aspects of deterrence.

Perhaps the most enduring criticism of the

original neoclassical model is that it is static

and, accordingly, does not explicitly allow

for individuals to differ in their time prefer-

ences, a fact that is inspired by a generation

of behavioral economics research and was

noted in early work by Block and Heineke

(1975) and Cook (1980). Dynamic exten-

sions of Becker can be found in Polinsky and

Shavell (1999), Lee and McCrary (forthcom-

ing), and McCrary (2011), among others.

An abbreviated version of such a dynamic

model was presented in section 1 of this

article. The most important insight arising

from the dynamic corollary of Becker is that

individuals who are myopic and engage in

hyperbolic discounting will be more strongly

deterred by changes in p , which affect util-

ity immediately, than by changes in f , which,

for the most part, affect utility in the distant

future. To the extent that offenders tend to

be hyperbolic discounters and there is ample

evidence that many are, we should not expect

long sentences to deter to nearly the same

degree as changes in the probability of either

arrest or some type of punishment.

In recent years, the Becker model has

also been augmented to allow for the fact

Chaln and McCrary: Criminal Deterrence: A Review of the Literature

that sanctions do not necessarily follow

from apprehension. Indeed, many offend-

ers are arrested but do not suffer anything

more than a trivial criminal sanction, either

because charges are not led or are dis-

missed or because a custodial sentence is

not handed down or is considered already

served at the time of sentencing. Likewise,

behavioral scientists have incorporated

the notion that individuals suffer both

legal and nonlegal sanctions when they are

arrested for committing a crime (Nagin and

Paternoster 1994, Nagin and Pogarsky 2003,

and Williams and Hawkins 1986). While the

effect of legal sanctions has been well under-

stood since Becker, the extent to which indi-

viduals suffer social stigma and other social

costs as a result of an arrest remains less well

understood. Nagin (2013) formalizes these

related concepts within the framework of

the Becker model, conceptualizing p as the

product of the probability of apprehension,

, and the conditional probability of a sanc-

tion given apprehension, P

(S|a)

. With these

additions, it can be shown that if individuals

are more sensitive to changes in the prob-

ability of apprehension than to changes in

the sanction, it is easy to see that changes

in utility (and thus crime) can potentially be

explained by informal sanction costs alone

(Nagin 2013). A related and arguably more

important insight is that in the event that

informal sanctions costs are very high, it is

considerably more likely that deterrence will

accrue via the probability of apprehension

( p

) than via f .

Related to this is the issue of hetero-

geneity in individual utility over the legal

sanction. In the event that the stigma of a

custodial sentence declines, the disutility

derived from punishment will fall as a func-

tion of the length of an individual’s criminal

history (Durlauf and Nagin 2011 and Nagin,

Cullen, and Jonson 2009). That is, as indi-

viduals accumulate a longer criminal record,

the stigma from being labeled a “criminal”

may lose its effectiveness and thus no longer

represent an important component of the

cost of committing a crime. Likewise, the

disutility of punishment may decline due to

the presence of informal social networks that

develop as a result of an individual’s prison

experience. Hence, to the extent that a large

proportion of crime is committed by repeat

offenders, crime should be more sensitive to

the probability of apprehension than to the

severity of sanction, because repeat offend-

ers have already paid the informal costs asso-

ciated with being labeled a criminal.

A nal extension that merits discussion can

be found in Durlauf and Nagin (2011) who

consider that, in addition to exhibiting strong

time preferences, individuals may have a

great deal of trouble accurately estimating

the risk of apprehension even after updat-

ing in response to new information (Anwar

and Loughran 2011). Durlauf and Nagin

develop a model in which p is probabilistic

and show that for a xed sanction, when p

is perceived to be arbitrarily close to either

zero or one (i.e., the probability of apprehen-

sion is thought to be either extremely low or

extremely high), the effect of certainty will

be greatest. This follows from the tendency

of individuals to systematically overestimate

the likelihood of rare events (e.g., a terrorist

attack) and underestimate the likelihood of

more common events (e.g., a car accident).

As Durlauf and Nagin note, in a world in

which the perceived probability of detection

is very low, even small changes in that per-

ceived probability can have correspondingly

large effects, thus potentially rationalizing

large behavioral responses to hot-spots polic-

ing and deterrence advertising.

The implication that the response of crime

to p might be systematically greater than to

f has far-reaching implications with respect

to public policy. First, given that deterrence

is cheap relative to incapacitation, ef-

cient resource allocation demands a shift in

resources toward more deterrence-intensive

Journal of Economic Literature, Vol. LV (March 2017)

inputs. Hence, in deciding how to allocate

criminal-justice resources between police

ofcers and prisons, the available evidence

suggests that money is best spent on police

ofcers, as well as perhaps on jails that might

be used to detain individuals who receive

short sentences. Second, it may be possible to

reduce the size of the US prison population,

which has grown six-fold over the course of

a generation, without compromising public

safety. Such an idea is strongly supported by

the small crime–prison population elastici-

ties reported by Liedka, Piehl, and Useem

(2006) and Johnson and Raphael (2012), as

well as Lofstrom and Raphael’s recent eval-

uation of the effects of California’s realign-

ment policy. It is also articulated anecdotally

by the experience of New York State, which

has both reduced its prison population as

well as its crime rate in recent years.

These points are underscored in Kleiman

(2009) and Durlauf and Nagin (2011), among

others, who ask the provocative question as

to whether both imprisonment and crime

can be simultaneously reduced. The answer

to this question lies in the magnitude of the

relevant deterrence elasticities, the specics

of which are cleverly illustrated in Durlauf

and Nagin, who ask us to consider a world

in which there are two types of criminal

opportunities: desirable opportunities with

corresponding probability of arrest, p

, and

undesirable opportunities with probability of

arrest, p

. Initially, individuals who encoun-

ter a criminal opportunity that is assigned

probabilistically elect to commit a crime

if p > p

∗

, where p

∗

> p

. Hence,

all available opportunities are acted upon.

Durlauf and Nagin next ask us to consider

that p

and p

are each increased by a fac-

tor g such that g p

< p

∗

< g p

. In this

world, only the desirable opportunities will

be acted upon. The obvious result is that

crime declines as all of the opportunities

are no longer attractive. What is less obvi-

ous is that clearance rates fall by construc-

tion and, accordingly, so do the number of

arrests and subsequent incarcerations. As

shown in Blumstein and Nagin (1978), if the

magnitude of either the elasticity of crime

with respect to p or f is less than 1, then the

decline in the crime rate associated with an

increase in p and f will not be sufciently

large to avoid an increase in the incarceration

rate. The key then is to determine whether

there are policies that satisfy this inequality

or, in the absence of such policies, identify

policies that decrease severity but have large

elasticities with respect to p . In our view,

swift-and-certain sanctions regimes such as

that motivated by HOPE and visible police

presence are two such policies that seem

especially promising.

6.2 Directions for Future Research

As each of the deterrence literatures has

matured, researchers can begin to focus

less attention on standard identication

problems and more attention on identifying

mechanisms. This is not to say that causal

identication is unimportant. On the con-

trary—we believe it is as important as it

ever was. However, in the past decade, great

strides have been made in selecting strategies

that credibly identify the causal effects of a

variety of policies which has, in turn, gener-

ated a consensus on the range of effects that

can be expected to accrue from a given type

of intervention. For several literatures that

have generated no such consensus (e.g., cap-

ital punishment), we strongly suspect that

there is little progress to be made in rehash-

ing the same state-level data. Accordingly,

we provide three recommendations to guide

future work.

First, there is a large and growing liter-

ature that supports the deterrence value

of hot-spots policing and, in particular, the

importance of a visible police presence.

However, the evidence on the effects of

disorder policing is more mixed and the

idea remains highly controversial. The best

Chaln and McCrary: Criminal Deterrence: A Review of the Literature

evidence suggests that cleaning up physi-

cal disorder is important, but it is not clear

whether broken-windows policing is a nec-

essary ingredient to this strategy. Given the

inherent risks of broken-windows policing

that accrue to both ofcers and citizens and

the civil-rights concerns that are intrinsic in

such a strategy, future research is needed

to identify the extent to which broken win-

dows reduces crime and, if so, whether those

reductions are due to deterrence or inca-

pacitation. There continues to be substan-

tial disagreement on this topic and, while

we are skeptical of the deterrence value of

broken windows, our reading of the research

literature is that we do not yet have suf-

cient information to make an informed

recommendation.

Second, while evidence is building that

swift-and-certain sanctions can deter offend-

ing at dramatically lower costs for both society

and offenders, the idea requires additional

testing. In particular, the conditions under

which such programs work and the degree

to which they are replicable and scalable

remains unknown. It has been suggested that

the success of such programs often depends

on the existence of effective leaders and an

unusual degree of cooperation among local

policy makers. Moreover, swift-and-certain

sanctioning only works if offending can be

reliably detected in the rst place.

A third area that, in our view, will benet

from greater research, concerns the deter-

rence value of investments in private pre-

cautions by potential victims. Evaluations

of LoJack (Ayres and Levitt 1998) and

business improvement districts (Cook and

MacDonald 2011) establish that private

investments can deter (after all, they can-

not incapacitate). However, we know rela-

tively little about the effects of other types

of private behavior, such as investments in

burglary alarm systems, the emergence of

various smart phone apps that provide infor-

mation, as well as other types of technology.

In closing, we note that Gary Becker’s

recent passing prompts us to acknowledge

yet again the decisive impact of his landmark

1968 paper. As his ubiquity in this review

makes clear, his insights launched an entire

literature—one that has had and contin-

ues to have profound implications for, and

impact on, public policy and safety.

R

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