Njdoc Megans Law Assessing the Practical and Monetary Efficacy 2008

• Topics: Discrimination, Sex Offenders (Discrimination)

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The author(s) shown below used Federal funds provided by the U.S.
Department of Justice and prepared the following final report:

Document Title:

Megan’s Law: Assessing the Practical and
Monetary Efficacy

Author:

Kristen Zgoba, Ph.D.; Philip Witt, Ph.D.; Melissa
Dalessandro, M.S.W.; Bonita Veysey, Ph.D.

Document No.:

225370

Date Received:

December 2008

Award Number:

2006-IJ-CX-0018

This report has not been published by the U.S. Department of Justice.
To provide better customer service, NCJRS has made this Federallyfunded grant final report available electronically in addition to
traditional paper copies.

Opinions or points of view expressed are those
of the author(s) and do not necessarily reflect
the official position or policies of the U.S.
Department of Justice.

The Research & Evaluation Unit
Office of Policy and Planning
New Jersey Department of Corrections

Megan’s Law: Assessing the Practical and Monetary Efficacy
Grant Award # 2006-IJ-CX-0018
National Institute of Justice
Principal Investigator:
Kristen Zgoba, Ph.D.
New Jersey Department of Corrections
Research & Evaluation Unit
Trenton, NJ 08625
(609) 777-4256
kristen.zgoba@doc.state.nj.us
Co-Principal Investigator:
Philip Witt, Ph.D.
Associates in Psychological Services, P.A.
25 N. Doughty Ave.
Somerville, NJ 08876
(908) 526-1177
phwitt@optonline.net
Research Assistant/ Data Manager:
Melissa Dalessandro, M.S.W.
New Jersey Department of Corrections
Research & Evaluation Unit
Trenton, NJ 08625
(609)-292-5670
melissa.dalessandro@doc.state.nj.us
Consultant:
Bonita Veysey, Ph.D.
Rutgers University
School of Criminal Justice
Newark, NJ 07102
veysey@andromeda.rutgers.edu

This document is a research report submitted to the U.S. Department of Justice. This report has not
been published by the Department. Opinions or points of view expressed are those of the author(s)
and do not necessarily reflect the official position or policies of the U.S. Department of Justice.

MEGAN’S LAW

EXECUTIVE SUMMARY
The research that follows concerns the various impacts of community notification and
registration laws (Megan’s Law) in New Jersey. Although this report includes a variety of
interesting findings and many ideas that will be explored upon post grant period, this research
was embarked upon, in general, to investigate: 1) the effect of Megan’s Law on the overall rate
of sexual offending over time; 2) its specific deterrence effect on re-offending, including the
level of general and sexual offense recidivism, the nature of sexual re-offenses, and time to first
re-arrest for sexual and non-sexual re-offenses (i.e., community tenure); and 3) the costs of
implementation and annual expenditures of Megan’s Law.

These three primary foci were

investigated using three different methodologies and samples.
Phase One was a 21-year (10 years prior and 10 years after implementation, and the year
of implementation) trend study of sex offenses in each of New Jersey’s counties and of the state
as a whole. In Phase Two, data on 550 sexual offenders released during the years 1990 to 2000
were collected, and outcomes of interest were analyzed.

Finally, Phase Three collected

implementation and ongoing costs of administering Megan’s Law.
The following points highlight the major findings of the three phases of the study.


New Jersey, as a whole, has experienced a consistent downward trend of sexual offense
rates with a significant change in the trend in 1994.



In all but two counties, sexual offense rates were highest prior to 1994 and were lowest
after 1995.



County trends exhibit substantial variation and do not reflect the statewide trend,
suggesting that the statewide change point in 1994 is an artifact of aggregation.



In the offender release sample, there is a consistent downward trend in re-arrests,
reconvictions and re-incarcerations over time similar to that observed in the trend study,
except in 1995 when all measures spiked to a high for that period. This resulted in

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significant differences between cohorts (i.e., those released prior to and after Megan’s
Law was implemented).


Re-arrests for violent crime (whether sexual or not) also declined steadily over the same
period, resulting in a significant difference between cohorts (i.e., those released prior to
and after Megan’s Law was implemented).



Megan’s Law has no effect on community tenure (i.e., time to first re-arrest).



Megan’s Law showed no demonstrable effect in reducing sexual re-offenses.



Megan’s Law has no effect on the type of sexual re-offense or first time sexual offense
(still largely child molestation/incest).



Megan’s Law has no effect on reducing the number of victims involved in sexual
offenses.



Sentences received prior to Megan’s Law were nearly twice as long as those received
after Megan’s Law was passed, but time served was approximately the same.



Significantly fewer sexual offenders have been paroled after the implementation of
Megan’s Law than before (this is largely due to changes in sentencing).



Costs associated with the initial implementation as well as ongoing expenditures continue
to grow over time. Start up costs totaled $555,565 and current costs (in 2007) totaled
approximately 3.9 million dollars for the responding counties.



Given the lack of demonstrated effect of Megan’s Law on sexual offenses, the growing
costs may not be justifiable.

MEGAN’S LAW

INTRODUCTION
On July 29, 1994, Jesse Timmendequas, a sex offender who had been released after
serving a maximum sentence in a New Jersey correctional facility, raped and murdered sevenyear-old Megan Kanka in Hamilton, New Jersey. The intense community reaction that followed
extended well beyond the state. One expression of community outrage was the enactment of
laws to notify the public of the presence of sex offenders living and working in their community.
The premise was, and still is, that with this knowledge, citizens will take protective measures
against these nearby sex offenders. As Beck, Clingermayer, Ramsey and Travis (2004) note,
“Exactly what action is expected is not clear, but it is hoped that, armed with this critical
information, citizens will work on their own or in concert with government to make their
neighborhoods safer” (p. 142).
During the following decade, all 50 states and the District of Columbia enacted some
version of such community registration and notification laws, collectively referred to as
“Megan’s Laws” (Presser & Gunnison, 1999; Zevitz & Farkas, 2000). Although a few states,
such as Washington, had enacted community notification laws prior to 1994, the federalization
of community notification laws in 1996 created strong incentives for other states to follow suit
(Presser & Gunnison, 1999).
The legislation known as Megan’s Law, includes both registration and notification. Sex
offenders must register their addresses with local police jurisdictions within a specified time of
release from prison. By way of the registration process, the public is then notified of the
offender’s presence in the neighborhood. The goal of notification is to inform both the public and
past victims so that they can protect themselves accordingly. As with other states, registration

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and notification are separate steps in New Jersey, but are often referred to as one process. In
New Jersey, offenders are placed into one of three tiers, representing a hierarchy of potential risk
of an offender’s re-offense. A risk assessment instrument is used to predict the offender’s
likelihood of re-offense, which ultimately determines placement into the tier. Tier one represents
the lowest risk and requires only notification of law enforcement officials and the victims.
Offenders are considered low risk and eligible for a tier one placement if they received a low risk
assessment score and are on probation/ parole, receiving therapy, employed and free of alcohol
and drugs. A tier two classification represents a moderate risk of a re-offense. It requires
notification of organizations, educational institutions, day care centers and summer camps. The
factors for placement into a tier two category include a moderate to high risk assessment score,
failure to comply with supervision, lack of employment, abuse of drugs or alcohol, denial of
offenses, lack of remorse, history of loitering or stalking children and making threats (Brooks,
1996; Matson & Lieb, 1997; Witt & Barone, 2004). Tier three offenders are those who are
predicted to present the greatest risk to re-offend. This category has generated the most legal
resistance because it calls for the broadest level of notification. The entire community is notified
through posters and pamphlets. The factors necessary for the placement into a tier three category
are a high probability of re-offending evidenced by a particularly heinous instant offense or a
high-risk assessment score, repetitive and compulsive behavior, sexual preference for children,
failure or refusal of treatment, denial of the offense and lack of remorse (Brooks, 1996; Rudin,
1996; Witt & Barone, 2004).
Despite their existence for over a decade, little work has been done to examine the
effectiveness of these laws on sexual offense rates. A few researchers, such as Beck and
colleagues (2004), have conducted surveys to determine what protection methods community

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members use when given information regarding the presence of sex offenders. Beck and
colleagues (2004) approach their research from the viewpoint that community notification laws
were enacted more to change the behaviors of potential victims than those of potential sexual
recidivists. In this study, Beck and colleagues (2004) differentiated between two types of
protective measures: (1) “self-protective measures,” or behavioral measures initiated by the
potential victims themselves; and (2) “altruistic protective measures,” or behavioral measures
initiated by family members to protect other household members (e.g., their children) (Beck &
Travis, 2002). These studies found that community notification did, in fact, increase altruistic
behaviors by community members to protect members of their households, although the findings
are inconsistent with regard to whether self-protective behaviors increased after community
notification. Because of these results, Beck and colleagues (2004) posit that it is not the
enactment of community notification laws themselves that influences protective behaviors, but
the community members’ perceived risk of victimization (also measured in these surveys) that
mediates these behaviors. This mediating factor presents problems for identifying the true effect
of these laws on sexual recidivism rates.
A few studies have also surveyed sex offenders to determine the impact that community
notification laws have had upon them. Tewksbury (2005) found that social stigmatization, loss of
relationships, employment and housing, and both verbal and physical assaults were experienced
by a significant minority of registered sex offenders (see also Tewksbury & Lees, 2006). Zevitz
and Farkas (2000) also found that a majority of sex offenders reported negative consequences,
such as exclusion from residences, threats and harassment, emotional harm to their family
members, social exclusion by neighbors, and loss of employment. Furthermore, according to
many tier three offenders interviewed, these laws would not deter them from committing future

MEGAN’S LAW
sex offenses (Zevitz and Farkas, 2000). In fact, Presser and Gunnison (1999) suggest that
notification laws may be counterproductive in that public scrutiny causes additional stress to
offenders who are transitioning back into the community. The fear of exposure may cause
offenders to avoid treatment, and in the case of pedophiles, may encourage offenders to seek out
children as a result of adult isolation. If these assumptions are true, the risk of recidivism may be
increased (Presser & Gunnison, 1999), or at least such factors would work against any protective
measures taken, thus lessening or eliminating any positive effect of the law.
None of the aforementioned research, however, addresses the critical question of whether
community notification and registration laws actually reduce sex offense rates (primary offenses
or re-offenses) in the communities in which the laws are applied, or what patterns of sexual
offense rates appear. Despite Megan’s Laws being in effect in all 50 states, only one study was
found that examines pre- and post-Megan’s Law sex offense rates. That study, conducted in the
state of Washington, compared sexual recidivism rates between two groups of sexual offenders:
one released three years prior to the implementation of community notification laws in that state,
and one released three years after the implementation. The pre- and post- target groups were
those most likely to be affected by the law (i.e., those who would qualify for tier three
classification). To account for population differences, offenders in both groups were matched on
the number of sex convictions and the type of victim (i.e., adult or child) (Schramm & Milloy,
1995). Their analysis of potential group differences revealed that at the end of 54 months (fourand one- half years “at risk”), there was no statistically significant difference in the arrest rates
for sex offenses between the two groups (19 percent versus 22 percent). However, the study did
find that notification had an effect on the time of the next arrest for any type of offense.

MEGAN’S LAW
Offenders subject to notification were arrested for new crimes much more quickly than were
offenders not subject to notification. (Schramm & Milloy, 1995).
These results suggest that Megan’s Laws may not be effective in reducing recidivism
rates. One can make a case, in fact, that Megan’s Law, at least as implemented in Washington,
had no effect on the rate of sex offense recidivism, although it may result in more rapid detection
of new sex offenses (see discussion in Pawson, 2002).
This lack of outcome studies means that Megan’s Laws constitute an untested mandate in
the domain of empirical research. Despite widespread community support for these laws, there
is virtually no evidence to support their effectiveness in reducing either new first-time sex
offenses (through protective measures or general deterrence) or sex re-offenses (through
protective measures and specific deterrence).
The study described below investigates various impacts of community notification and
registration laws (Megan’s Law) in New Jersey. The primary areas of study are: 1) the effect of
Megan’s Law on the overall rate of sexual offending over time; 2) its specific deterrence effect
on re-offending, including the level of general and sexual offense recidivism, the nature of sexual
re-offenses, and time to first re-arrest for sexual and non-sexual re-offenses (i.e. community
tenure); and 3) the costs of implementing and maintaining Megan’s Law. These three primary
foci were investigated using three different methodologies and samples.
Phase One was a 21-year (10 years prior and 10 years after implementation, and the year
of implementation) trend study of sex offenses in each of New Jersey’s counties and the state as
a whole. In Phase Two, data on 550 sexual offenders released during the years 1990 to 2000
were collected, and outcomes of interest were analyzed.

Finally, Phase Three collected

implementation and ongoing costs of administering Megan’s Law.

MEGAN’S LAW

PHASE ONE: THE TREND STUDY
This study attempts to remedy one aspect of the gap between the lack of research and the
legislation, by examining the trend of sexual offense rates between and within the 21 counties of
New Jersey from 1985 through 2005. The study was conducted in New Jersey, the state in which
Megan Kanka was a victim and the subsequent origin of Megan’s Law. Phase One is a trend
study, which will provide information on whether statistical differences exist in sex offending
arrests before and after the implementation of Megan’s Law.
The trend analysis focuses on the pattern of sexual offense rates in New Jersey over a 21year timeframe while comparing them to drug offense rates and non-sexually based offending
rates. The data represent crime rates for the state as a whole and for each of the 21 counties for
the ten years prior to the legislation and the ten years after the enactment of the legislation and
includes the first full year in which Megan’s Law was implemented (i.e., 1995).
Methods
The purpose of this study is to determine whether Megan’s Law had an effect on the rate
of sexual offending in New Jersey. Several different analyses were conducted to answer this
primary question. First, a trend analysis of New Jersey sex offense rates pre- and post-Megan’s
Law implementation provides both a visual and statistical test of effectiveness.

Second,

aggregation sometimes masks important differences at a lower level. Therefore, the same trend
analyses were conducted on each of the 21 counties in New Jersey. Third, historical effects
broader than that solely for sex offenses may be responsible for observed changes (i.e., an
observed effect of Megan’s Law may be spurious). Two comparative analyses at the state level

MEGAN’S LAW
were conducted to contrast changes in rates of sex offenses to other offenses (i.e., drug and other
non-sex/non-drug) over the same period of time. These additional analyses were made in an
effort to place sex crimes in the context of overall crime and a specific crime (drugs) that has
been subjected to several types of legislation.
Sample and Data Collection
This study is based upon a simple pre-post research design to determine whether any
significant changes in the rates of sexually based offenses reported by law enforcement agencies
occurred after the implementation of New Jersey’s Megan’s Law in late 1994. Rates for sexually
based offenses, non-sexually based offenses, and drug offenses were collected for the years 1985
through 2005 in order to construct a comparative trend analysis. Data for the three types of
crime were collected for all 21 counties of New Jersey, using Uniform Crime Report (UCR)
numbers for years 1985 through 2005. Prevalence rates for the three offense categories were
established using population estimates from the Department of Labor’s Bureau of Labor
Statistics. The Department of Labor’s population estimates for New Jersey were cross-referenced
with the Sourcebook of Criminal Justice Statistics, a yearly federal government publication.
Because no significant differences in population estimates were found between these two
sources, UCR numbers were used for trend analyses conducted in this study. In order to compare
state and county trends in sexually based offenses, non-sexually based offenses, and drug abuse
violations, UCR aggregate numbers and prevalence rates for years 1985-2005 were entered into
an Excel spreadsheet and SPSS.
Definitions and Measures
Uniform Crime Report statistics are based upon number of arrests, and as such, use of the
term “offenses” in this study refers to number of reported arrests. Three crime categories were

MEGAN’S LAW
used for trend analysis comparisons: 1) sexually-based offenses, 2) drug offenses, and 3) other
offenses (non-sex/non-drug). Analyzing the single set of sex offense rates for the 21-year time
span provides an initial test of rate change.

Across the US, crime rates in general have been

dropping since the late 1990’s. The inclusion of all New Jersey non-sex/non-drug crime rate
trends presents a visual contrast: (1) to confirm/disconfirm the national trends, and (2) to
contextualize the sex offense rate trend within the general trends. Other offenses allow a control
for New Jersey specific historical factors that might influence rates across crime categories, such
as increased or decreased enforcement or prosecutorial budgets, the number of police or
probation officers, or aggressiveness of prosecutors’ and the State Attorney General’s offices.
Drug offenses, like sex offenses, have been the target of law enforcement policies.
Although drug offense rates may change over time based upon what drugs are most common,
drug arrests rates are also particularly vulnerable to changes in federal and local policies and law
enforcement efforts. Furthermore, although the contrast between drug and sex crimes may not
be immediately obvious, the inclusion of drug offense rate trends provides an opportunity: (1) to
demonstrate the variations in rates over time, and (2) to evaluate whether these variations have
similar patterns to those of sex offense rates.1
These crime categories were based on the state’s Uniform Crime Report (UCR), a yearly
statistical report based upon crimes reported to law enforcement agencies throughout the State of
New Jersey. Definitions of certain sexually-based crimes, such as rape, were clarified via phone
interviews with the New Jersey State Police in December 2006. In addition, legal definitions of
specific crimes (e.g. endangering the welfare of a child) were verified by reviewing Title 2C of
the New Jersey Code of Criminal Justice in LexisNexis Academic. Because “Rape” is designated

No formal statistical tests were performed contrasting drug and non-sexual offense rates to sex offense rates. The
visual displays are used to provide general contrasts, only.
This document is a research report submitted to the U.S. Department of Justice. This report has not
been published by the Department. Opinions or points of view expressed are those of the author(s)
and do not necessarily reflect the official position or policies of the U.S. Department of Justice.

MEGAN’S LAW
as a separate category by the UCR, the UCR categories “Rape” and “Sex Offenses” were
combined under the category “Sexually-Based Offenses” for the purposes of this study. The
category “Non-Sexually Based Offenses” is comprised of all UCR categories except “Rape”,
“Sex Offenses” and “Drug Offenses”. Furthermore, “Drug Offenses” included various types of
drug crimes, such as the manufacturing and distribution of controlled substances, possession with
the intent to sell and distribution of a controlled substance within a school zone.
Analytic Strategy
In studies of this type, typically a simple pre-post test of rates is conducted to determine
whether an intervention is successful. Given that these data are points in time, namely crime
rates by year, time based strategies are commonly used, including time-series/ARIMA models
and regression discontinuity designs that allow for temporal autocorrelation. These analyses are
constructed based upon a known change point. Although it is known that Megan’s Law was
passed in late 1994, it is not known when the agencies charged with implementing the law were
fully prepared to do so. Further, Megan’s Law may not have been uniformly implemented
across the state at a standardized point. The earliest change point that might be attributed to the
legislation, therefore, is between 1994 and 1995. Given delayed implementation, the true effect
of the legislation may occur during a subsequent year. For this study, a method is required that
will allow for the detection of such delayed effects.
Several authors have considered the problem of change-points (see Pettitt, 1979 for a
brief review). Some make assumptions regarding the nature of the pre- and post-change sample
distributions. Most assume that the change-point is known. Pettitt (1979) offers a solution to the
crime trend problem by suggesting a method of determining the most probable point of change

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and using a non-parametric procedure to test for significance. The logic of Pettitt’s argument is
summarized below.
Assume a sequence of random variables; X1, X2, … , XT and a change-point at , where
Xt for t = 1, … , have a distribution function of F1(x) and Xt for t = +1, … , T have a
distribution function of F2(x) and F1(x)

F2(x). Since the change-point is unknown, T-1 two

sample comparisons are necessary. In the complete sample of T,
Ut,T = 2Wt – t(T +1)
where Wt is the sum of the ranks of all observations from 1 to t. This produces a U statistic for
each point in the time series comparing the mean of the series prior to t with the mean of the
series after t. A version of the Mann-Whitney U statistic, used to test that the two samples, X1,
… , Xt and Xt +1 , … , XT , come from the same population, is applied to the maximum U value:
KT = max Ut,T
1< t < T

The approximation of significance probabilities that is associated with KT is:
P

2 exp (-6k2/ (T 2 + T 3))

where the approximation holds accurate to two decimal places for p< .5 (Pettitt, 1979).
This analysis employs this technique used to determine significant differences when the
change point is unknown. This technique was selected specifically because we did not want to
make any assumptions regarding the implementation phase. In most cases, where a law requires
changes to procedure, the effect is likely to be delayed by some unknown period.
Data from the 21 New Jersey counties were entered separately, the New Jersey total was
aggregated from the counties’ summary numbers and the resulting rates were adjusted for yearto-year population changes at the state level. For each county and for the state as a whole, the

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yearly rates were rank ordered and a Mann-Whitney U test was performed to test for a change in
trend. Thus, for the state and for each county, every year is tested as a potential change point.

Results
The results are organized into two major sections. The first section presents the trend
analysis for both the state and for the individual counties. The second section contrasts the sex
offense trend rates to trends in other offenses (i.e., drug and other non-sex/non-drug) over the
same period of time.
County and Statewide Sex Offense Rates
Figure 1 displays the rates of sex offenses for New Jersey as a whole from 1985 to 2005.
The rates varied from 51 offenses in 1986 to a low of 29 offenses per 100,000 population in
2005. In general, there is a consistent downward trend.
Individual counties varied substantially both between counties and within counties over
time. Table 1 presents summary statistics of each county and the state as a whole. Counties
varied in population size from under 100,000 population in the smallest counties of Cape May,
Salem, and Warren, to over three quarters of a million residents in the largest counties of Bergen
and Essex. The population size of the county is not consistently related to the rate of sex
offenses. For example, one of the largest counties, Essex County (Newark), has a relatively high
rate of offenses (68), whereas the largest county, Bergen, has a relatively low rate (32). In
contrast, the highest rate of offenses is in one of the smaller population counties, Cumberland. In
the smallest counties, Cape May has a rate of 72 offenses per 100,000, whereas Warren has a rate
of 36.

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Figure 1. New Jersey Sex Offenses per 100,000 Population from 1985 to 2005

50
40
30
20
10

0
85

Rate per 100,000 Residents

Year

In 19 of the 21 counties, the year with the highest rate of sex offenses occurred before
1994; Passaic and Sussex Counties were the exceptions. In 19 of the 21 counties, the year with
the lowest rate of sex offenses occurred after 1995; Morris and Passaic Counties were the
exceptions in this case. The rank trend tests (Mann-Whitney U tests) revealed that (1) six
counties had no statistically significant change point (Bergen, Hunterdon, Mercer, Morris,
Passaic and Sussex), and (2) an additional six counties had a change point that preceded Megan’s
Law (Burlington, Camden, Monmouth, Salem, Somerset, and Union). This means that only nine
counties have a change point after Megan’s Law was passed with the years of change falling
between 1994 and 1998. One final observation of county contrast should be noted. In several
cases, counties had substantial drops in sex offenses after Megan’s Law was enacted. However,
This document is a research report submitted to the U.S. Department of Justice. This report has not
been published by the Department. Opinions or points of view expressed are those of the author(s)
and do not necessarily reflect the official position or policies of the U.S. Department of Justice.

MEGAN’S LAW
in the last several years these counties have had substantial increases in sex offense rates
(analyses not shown). This is true, for example, of Ocean, Hudson, and Warren Counties.

Table 1. County and State Summary Statistics for Sex Offenses
County
Population
Average
Highest
Lowest
in 1994
Rate
(Year)
(Year)
Atlantic
236,589
71.0
128 (1991)
31 (2005)
Bergen
848,392
32.0
71 (1988)
24 (2002)
Burlington
407,060
30.4
51 (1985)
16 (2002)
Camden
508,479
42.9
97 (1986)
29 (2005)
Cape May
99,561
72.2
111 (1992)
38 (2003)
Cumberland
144,544
91.1
127 (1992)
63 (2001)
Essex
784,460
67.6
95 (1990)
35 (2004)
Gloucester
242,161
35.6
53 (1993)
22 (2005)
Hudson
572,720
44.0
56 (1993)
33 (2001)
Hunterdon
113,522
18.3
32 (1985)
9 (2004)
Mercer
335,229
46.4
67 (1986)
35 (2005)
Middlesex
701,090
27.1
38 (1985)
19 (2004)
Monmouth
577,069
37.6
56 (1988)
25 (2002)
Morris
439,533
23.1
33 (1986)
15 (1993)
Ocean
461,152
24.7
38 (1993)
15 (2001)
Passaic
478,164
50.8
82 (1997)
36 (1988)
Salem
64,691
50.8
78 (1991)
27 (2005)
Somerset
262,243
18.9
27 (1988)
10 (1998)
Sussex
137,021
21.8
32 (1999)
12 (2005)
Union
504,864
30.0
53 (1986)
13 (2004)
Warren
95,762
36.4
63 (1987)
14 (2001)
NEW JERSEY
39.8
51 (1986)
29 (2005)
** Change point precedes implementation point

Change
Year
1994
n.s.
1993 **
1989 **
1995
1998
1994
1997
1998
n.s.
n.s.
1998
1992 **
n.s.
1996
n.s.
1992 **
1991 **
n.s.
1993 **
1996
1994

Also, many counties demonstrated a predictable “jump” after Megan’s Law was
implemented. After a large initial drop in rates, there was a large rebound in sexual offenses (but
not as high as pre-Megan’s Law levels), followed by a continued decline. One example of this
phenomenon is Cumberland County. As can be seen in Figure 2, the large dip at year 10 (1994)
is followed by a spike the following year and then returns to a downward trend. This spike in
sexual offenses most likely reflects increased surveillance and arrests, rather than increased
offending.

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Figure 2. Cumberland County Sex Offense Trend

120
100
80
60
40
20

0
85

Rate per 100,000 Residents

140

Year

Although individual counties vary, the aggregate state statistics indicate a significant
change in trend in the year 1994 (MW-U=110.0, p<.001). Figure 3 displays the rates before and
after the implementation of Megan’s Law. The upper line represents sex offenses for the years
1985-1994, and the lower line represents sex offenses for the years 1995-2005. Superimposed
on the yearly rates is a linear trend line. There are two important differences between these trend
lines. First, beginning in 1995 the rate of sex offenses never again approaches the pre-1994
levels (i.e., the intercept and average are different). Second, the slope is steeper in the postMegan’s law period. This is particularly notable, since sex offenses are low base rate crime. The
fact that the decrease accelerates as the number of crimes decreases is unexpected. In fact, one

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might expect that an effective intervention would exhibit diminishing returns over time. This is
not the case in this instance.

Figure 3. Comparison of Sex Offense Rates per 100,000 Before and After Megan’s Law

Rate per 100,000 Population

55
50
45
40

Pre-Megan's Law
Post-Megan's Law

35
30
25
20
1

Number of Years Before or After Implementation

Statewide Sex Offense Rates Compared to Non-sex/non-drug and Drug Offenses
The aggressiveness with which arrest, prosecution and surveillance of specific crimes is
pursued changes over time. After Megan Kanka’s death at the hands of a convicted sex offender,
public sentiment demanded an immediate and aggressive response by law enforcement, the
courts and corrections. However, sex offenses are not the only crimes to receive this type of
attention. The federal War on Drugs was experienced at the state and local level as well. Special
task forces and interdiction programs resulted in vast numbers of arrests. At the same time, the
crack epidemic hooked thousands of individuals. It is difficult to disentangle the effects of law
enforcement and prosecution efforts from addiction trends. In the case of sex offenses, the trend
in reduced rates of offending preceded Megan’s Law. The challenge of this analysis is to

MEGAN’S LAW
separate the effects of intervention from the existing rate reduction momentum. The first set of
analyses addressed this point. The second concern is to control for historical effects. Drug
offenses, like sex offenses, should reveal rate patterns consistent with intervention efforts. Other
crimes should be more resistant to these specialized influences, but sensitive to larger social and
political influences. The following analyses contrast the statewide sex offense trends with drug
and other non-sex/non-drug offense trends.
Figure 4 displays the rates of non-sex (non-drug) offenses. The average number of crimes
per 1,000 population is 50.0 with the highest rate of offending at 56 in 1989 and the lowest at 45
in 2003. As illustrated, there is a consistent increase in crime rates in the late 1980’s, followed
by a five- year decline. Over the next several years the rates increased again, only to drop to their
lowest levels in recent decades. For the last five years the rate has remained stable at about 45
crimes per 1,000. In these data, there is a significant change point in 1998 (MW-U=98.00;
p=.005), indicating that the levels of crime prior to 1998 were significantly higher than those
after 1998.
Figure 4. New Jersey Non-sex /Non-drug Offenses per 1,000 Population from 1985 to 2005

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Rate per 1,000 Residents

60
50
40
30
20
10

Year

Unlike general crime, drug-related crimes showed very different rates by year. On
average, there are 68 drug crimes per 10,000 population. This varied from a high of 89 in 1989
to a low of 52 in 1985. As can be seen in Figure 5, drug crimes spiked in 1989, then dropped
precipitously. Although the rates increased again following 1993, this never again approached
the 1989 rate. The most recent decline appears be to be stable at around 65 crimes per 10,000
and has not achieved the 1985-86 rates. There is no significant change point.
Figure 5. New Jersey Drug Offenses per 1,000 Population from 1985 to 2005

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Rate per 1,000 Residents

30
25
20
15
10
5

Year

The general decline in sex offenses in NJ is similar to that of non-sex/non-drug crimes.
However, the statewide change point for sex offenses occurred during the Megan’s Law
implementation year (i.e., 1994), whereas the change in trend for non-sex crimes occurred later,
in 1998. The wide year-to-year fluctuations in drug crimes in fact may reflect specific policy
and practice efforts, although those efforts were not sustained. In the case of sex offenses, the
statewide change occurred when it was predicted to change and has maintained its impact over
time.

PHASE TWO: SEX OFFENDER OUTCOME STUDY
Methodology
Phase Two of the National Institute of Justice grant used a sample of sex offenders
released from New Jersey Department of Corrections facilities (either the Adult Diagnostic and
Treatment Center [ADTC] or one of the general population facilities) before and after the

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implementation of Megan’s Law. Fifty sex offenders per year (25 from the ADTC and 25 from
the general population) were randomly selected for the period covering 1990 through 2000, 11
years in total. This yielded a sample of 550 cases.
For each of these cases, extensive demographic, clinical, institutional and service use,
criminal history, and crime offense characteristics information was collected. This provides an
opportunity to contrast outcomes (i.e., recidivism, time to failure, and harm variables) of
offenders arrested and released prior to the passing of Megan’s Law with offenders arrested and
released after the legislation passed.
This component analyzed pre-post group differences on three outcomes:

•
•
•

Reduced recidivism- including re-arrests, re-convictions, and re-incarceration;
Increased community tenure- including days to first arrest and days to first arrest for a
sexual offense; and/or
Reduced harm- including fewer sex offenses, less violent offenses, and fewer child
victims.

The following sections present offender characteristics, bivariate differences in characteristics,
and pre-post group outcomes.

Results
Demographic Characteristics
Table 2 displays the demographic characteristics of the sample.

The sample is

comprised only of males. Half of the sample is white with black and Hispanic offenders
accounting for 35% and 15%, respectively. Only 0.2% of offenders classified themselves as
“Other.” At release, offenders were 34 years of age (sd=12.2). Nearly half (49%) were married

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at one time and 66 percent had at least one child (including stepchildren). On average, each
individual had 1.9 children (sd=2.1).
Table 2. Demographic Characteristics of Sex Offenders (n=550)
%
Variable
Race
% white
50.5
% black
34.8
% hispanic
14.6
% other
0.2
Average Age
% Ever Married
49.0
% With Children
65.9
Average Number of Children
Education Level
% less than high school
50.3
% high school diploma/GED
33.6
% some college or more
16.1
% Ever Employed
62.8
Employment Type
% white collar/professional
7.8
% blue collar/skilled trade
75.4
% service industry
13.2
% other
3.6

Mean (sd)

34.1 (12.2)

1.9 (2.1)

Half of the sample never completed high school. Specifically, 14 percent only achieved
an eighth grade education, whereas 36 percent attended high school, but did not graduate.
Twenty-five percent completed high school and 8 percent obtained a GED. Sixteen percent had
some college education with 4 percent completing an Associate Degree or higher. Sixty-three
percent had an employment history of a year or greater prior to committing the offense. Although
most offenders reported some variety of employment history, the median years of employment
was considerably low, at less than three years of past employment. Of those who had been
employed, most had held unskilled or trade jobs (75%) or jobs in the service industry (13%). A
notable 8 percent held white-collar or professional jobs. Offenders’ prior employment income
was unable to be determined for 29% of the sample. Of those offenders reporting employment

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income, 25% reported an income of $20,000 or less, 5% reported an income of $21,000 to
$30,000, 3% reported an income of $31,000 to $40,000, 1% reported an income of $41,000 to
$50,000, and 0.5% reported an annual income of $50,000 or higher.
Clinical Characteristics
This section includes measures commonly associated with risk (e.g., history of abuse,
familial criminal justice involvement), behavioral health problems, and past treatment
experiences. Table 3 displays these measures obtained from an offender’s folder.
Table 3. Clinical Characteristics of Sex Offenders (n=550)
Variable
% With History of Child Abuse
% Raised in Two Parent Home Up to Age 13
% With Family Member Involvement in CJ System
% With History of Mental Health Problems
% With History of Drug Use/Abuse
% With History of Alcohol Abuse
% Received Mental Health Treatment
% Received Mandated Sex Offender Treatment in Prison
% Received Other Treatment Services in Prison

%
39.0
65.7
8.6
23.1
44.8
47.1
34.7
94.0
88.4

Most offenders were raised in either a traditional two-parent home (66%) or in a motheronly headed household (23%), and the majority of offenders did not report any history of child
abuse (61%). Twenty-six percent, however, reported having experienced sexual abuse as a child.
A large majority of offenders (91%) did not have any family members involved in the criminal
justice system.
Only 23 percent of offenders reported some type of past mental health problem. These
mental health issues included problems diagnosed in childhood (e.g., emotionally disturbed,
developmental disorder) as well as more common diagnoses problems such as depression. In
addition, a sizeable proportion of offenders had a drug or alcohol abuse history, with 45%
reporting a prior drug abuse problem and 47% reporting a prior alcohol abuse problem.

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Thirty-five percent reported having received mental health treatment in the past. Most
offenders (94%) were reported as receiving some type of sex offender treatment while
incarcerated. A majority of offenders (88%) also received treatment in addition to the standard,
mandated treatment groups. Types of adjunct treatment offered to inmates included adult basic
education classes, life/social skills groups (e.g. anger management), and drug and alcohol
counseling.
Offender Criminal History
Offender criminal history includes information on prior arrests. These data are presented
in Table 4.

In general, the men incarcerated for a sex crime were more likely to have been

engaged in previous non-sex crimes than in sex crimes per se. Sixty-five percent had a previous
arrest for a non-sex crime. On average, they had been arrested 3.4 times (sd=5.77) and were
arrested for the first time when they were 21.5 years old (sd=8.21). Only 27 percent had been
previously arrested for a violent crime with an average of .5 prior arrests (sd=1.07). Even fewer
(24%) had been arrested for a sex crime in the past, with an average number of .4 prior arrests
(sd=1.02). On average, these offenders were 24.8 years old (sd=9.01) at the time of their first
arrest for a sex crime. Only 6 percent had been arrested as a juvenile for a sex crime.
Table 4. Offender Criminal History
Variable
% with Any Prior Arrests
Average Number of Arrests
Average Age at First Arrest
% with Prior Arrests for a Violent Crime
Average Number of Arrests for Violent Crime
% with Prior Arrests for a Sex Crime
Average Number of Arrests for a Sex Crime
Average Age at First Arrest for a Sex Crime
% with a Juvenile Arrest for a Sex Crime

%
64.9

Mean (sd)
3.64 (5.77)
21.5 (8.21)

27.3
.50 (1.07)
23.5
.43 (1.02)
24.8 (9.01)
5.7

Target Offense Characteristics
This document is a research report submitted to the U.S. Department of Justice. This report has not
been published by the Department. Opinions or points of view expressed are those of the author(s)
and do not necessarily reflect the official position or policies of the U.S. Department of Justice.

MEGAN’S LAW
Table 5 displays information regarding the sex crime(s) for which the men in the sample
were serving sentences. Eighty percent of offenders were serving time for child molestation
(incest=21% vs. non-incest=59%). Cases of rape and general exhibitionism accounted for 20%
and 0.4% of all cases, respectively.
Sixty-two percent of offenders denied committing certain acts of the instant crime, or
denied the sexual offense in its entirety. Most often, offenders in this latter group denied the
more egregious acts of the offense (i.e. penetration) or instances of multiple acts. According to
police reports, however, a majority of offenders (55%) engaged in multiple acts over a period of
time, and in 26 percent of the cases the offender had multiple victims.
The 550 offenders in the sample victimized a total of 796 individuals. That is an average
of 1.45 victims (sd=1.07) per offender for the current offense alone. However, this number is
skewed. In 74 percent of the cases, there was only one victim identified. Of the cases involving
two or more victims, the average number of victims was 2.7 (sd=1.49). Of the victims, 79
percent were female and 30 percent were male. These percentages include the cases where both
males and females were victims (8%). The mean age of victim in the index offense was 12.3
years old (sd = 9.74). Ages of victims spanned from 1 year to 87 years old; 65 percent of the
victims were 12 or younger, 24 percent were between 13 years old and 18, and the remaining 11
percent were 19 or older.
Table 5. Characteristics of Target Crime
%

Variable

Mean (sd)

Offense Type
% child molestation

79.5

% rape

20.2

% exhibitionism/voyeurism

0.4

% Offender Denied Some or All Aspects of Crime

62.2

% Cases Occuring Over Multiple Dates

55.2

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% Cases Involving Multiple Victims

26.0

Victim Gender
% male

21.1

% female

70.5

% both

8.4
12.3 (9.74)

Mean Age of Victim
Age Group of Victims

1.4 (1.1)

% 13 through 18

65.4
(1.1)
23.7

% 19 or older

10.9

% 12 and under

Relationship of Offender to Victim
% stranger

16.1

% family

48.2

% acquaintance

33.6

% significant other

2.2

% Lived With Victim

42.6

% Crime Occurred in Victim or Offender Home

77.2

% Cases Involving Weapon Use

13.2

Type of Weapon
% gun

27.3

% knife

51.5

% rope/tape/bondage

7.6

% other

13.6

% Drugs Involved in Crime
% Alcohol Involved in Crime
T

13.4
26.0

Most offenders had an established prior relationship with their victims, with only 16
percent of cases where the perpetrator was a stranger. In fact, nearly half (48%) of the
perpetrators were family members, with the remaining crimes committed by either acquaintances
of victims (34%) or victims’ significant others (2%). Further, 43 percent of offenders lived with
their victim(s) and in 77 percent of the cases the offense(s) were committed in the victim’s or
offender’s home (including shared residence).
In 13 percent of the cases a weapon was used. Of those cases, the most common weapon
used was a knife (52%), followed by a gun (27%), other weapon (14%) or the use of some form

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of restraint (8%). In 13 percent of the cases drugs were involved and alcohol was involved in 26
percent of the offenses.
Criminal Justice Factors
On average, offenders were sentenced to nearly nine years of incarceration (104 months,
sd= 63.8), with the most frequently imposed sentence being five years. The minimum and
maximum imposed sentences for the sample were one year and 36 years, respectively. In
actuality, offenders served approximately five years on average (56 months, sd=40.4), with time
served ranging from three months to 21.5 years. Only 32 percent of offenders were paroled
whereas 68 percent maxed out; leaving the prison with no post-incarceration supervision
requirements other than those imposed by Megan’s Law.
Table 6. Criminal Justice Factors
%

Variable

Mean (sd)
104.4 (63.8)
56.2 (40.4)

Mean Length of Sentence (in months)
Mean Time Served (in months)
32.4

% Paroled

Sample Equivalences
In studies that use random sampling it is assumed that the samples will be equivalent in
all relevant factors. This is, however, an assumption, and statistical theory suggests that although
rare, samples may be found to differ. In this case, it is known that samples differ temporally.
The differences in cohorts may be reflected in institutional responses (e.g., changes in court
procedures. In this case “Truth in Sentencing” legislation came into effect during this period),
social or community behavior (e.g., increases or drops in specific drugs of choice or type of
crime), or other historical sociopolitical changes. Bivariate analyses were conducted to confirm
offender similarity in: demographics, risk factors, and prior criminality; all known to associated
with the likelihood of recidivism.

MEGAN’S LAW
No statistically significant differences were found in demographic characteristics. Among
the risk factors, only receipt of other treatment services was significant (with the earlier cohort
more likely to have received services [95% vs. 83%; χ2 = 14.6, df=1, p<.001]). In terms of
criminal history, no variable was found to be significant except for the average number of prior
sex offenses (with the earlier cohort averaging a higher number [.56, sd=1.16 vs. .32, sd=.87;
F=7.21, df=1, 546, p=.007]). Among the target offense variables, only alcohol use was
significant (with the earlier cohort more likely to have used alcohol during the commission of the
crime [31% vs. 22%; χ2 = 6.09, df=1, p=.014]). Thus of the over fifty variables analyzed, only
three were significantly different between groups. Again appealing to statistical theory, with
multiple tests there is an increased likelihood of detecting significant relationships. No correction
was made in these analyses to account for this threat. However, given the vast number of
equivalencies, these groups are assumed equal for purposes of the outcome analyses.
Offender Outcomes Pre- and Post-implementation of Megan’s Law
Before presenting pre-post contrasts that are controlled by time at risk, year-by-year
graphs demonstrate several important points that must be kept in mind when interpreting the
remainder of the analyses. The outcome measure of recidivism was collected through June 15,
2007. The remaining measures were adjusted to assure that all offenders had an equal time at
risk, specifically 2,358 days or approximately six and a half years. Figure 6 presents the percent
of offenders released in each year who generally recidivate within the follow-up period (i.e., 6 ½
years). In this case, this figure presents the percent of persons who are re-arrested, the percent of
the sample re-convicted and the percent re-incarcerated. Clearly, these are three closely linked
outcome measures (e.g., conviction cannot occur in the absence of a chargeable offense).

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Figure 6. General Recidivism by Year
70
60
50
% Arrested

% Convicted
30

% Incarcerated

20
10
0
90

Year

Overall, 46 percent of offenders were re-arrested (9 percent were re-arrested for a sex
crime), 41 percent were convicted, and 35 percent were re-incarcerated. Although the figure
shows substantial movement up and down over time, there are no significant differences by year
(this is largely a power problem). Further, excluding the year 1995, all measures of recidivism
are declining over time from highs in the 50 to 60 percent range in the 1990 release cohort to the
25 to 40 percent range in the 2000 release cohort. What is interesting about this figure, however,
is the rates relative to each other within year. In most years, a stable percentage of persons who
are arrested are convicted. In this sample, over the 11 years, 88 percent who are arrested are
convicted. Of those convicted, 86 percent are incarcerated as a result. However, these rates vary
from year to year. For example, of the 1993 release cohort 46 percent were re-arrested; of those,
96 percent were convicted; and of those convicted 96 percent went back to prison.

comparison, of the 1995 release cohort, 56 percent were re-arrested and nearly all were convicted
(96%), but only 70 percent of those convicted were re-incarcerated. It is not clear from these
data whether the year-to-year differences are a result of procedural and administrative changes or
a reflection of a system response to public pressure.
This document is a research report submitted to the U.S. Department of Justice. This report has not
been published by the Department. Opinions or points of view expressed are those of the author(s)
and do not necessarily reflect the official position or policies of the U.S. Department of Justice.

MEGAN’S LAW
Recidivism
Table 7 presents the comparisons of the pre- and post-implementation groups on all
outcome measures, including recidivism, community tenure and harm (sexual re-offending). In
the first “recidivism” section, all measures (i.e., arrest, conviction and incarceration) are
significant. In all three variables, the post-implementation group has a lower percentage of cases
that have experienced the outcome.

This is for general recidivism. Forty-one percent of the

post-implementation group was re-arrested compared to 50 percent of the pre-implementation
group (χ2= 3.94, 1 df, p=.047). Similarly, 34 percent of the post-implementation group was
convicted compared to 46 percent of the pre-implementation group (χ2= 8.59, 1 df, p=.003). And
29 percent of the post-implementation group was re-incarcerated compared to 40 percent of the
pre-implementation group (χ2= 7.53, 1 df, p=.006).
Table 7. Offender Outcomes Pre and Post Megan’s Law Implementation (n=550)
Variable
Recidivism
% re-arrested any crime
% re-convicted at least once
% re-incarcerated at least once
Community Tenure
Days to arrest any crime (sd)
Days to arrest sex crime (sd)
Harm
% re-arrested sex crime
Sex crime type (n=48)
% child molestation
% rape
% other (voyeurism, exhibitionism)
% violent

Pre

Post

Total

Χ2/F (df)

sig.

49.7
46.3
40.0

41.2
34.0
28.8

45.8
40.7
34.9

3.94 (1)
8.59 (1)
7.53 (1)

.047
.003
.006

772.2(636.9)
813.7(690.5)

726.0(616.5)
765.3(706.0)

753.3(627.8)
794.9(689.6)

.329(1,250)
.056(1,47)

n.s.
n.s.

10.0

7.6

8.9

.97 (1)
1.70 (2)

n.s.
n.s.

54.5
13.6
31.8
31.9

66.7
20.0
13.3
20.5

59.5
16.2
24.3
26.7

9.01 (1)

.003

Community Tenure
Time to failure is an important outcome measure. Situations may exist where equal
percentages of experimental and comparison groups demonstrate an outcome, in this case, rearrest, but the average length of time to the arrest differs. Even in the case where equal
percentages of pre- and post-implementation subjects are re-arrested, more days in the

MEGAN’S LAW
community without committing a crime2 reflects improved outcomes in community and personal
harm, as well as cost savings.
The average time to an arrest for any type of crime was 753 days (sd=628) or about two
years, one month (see Table 6). There was no significant difference by implementation cohort.
The average time to an arrest for a sex offense was 795 days (sd=690) or about two years, two
months. There was no significant difference by implementation cohort for this variable.
A survival analysis was also conducted on these data to determine whether the rate of
failure by time at risk varies significantly by implementation cohort. Figure 7 displays the
survival curves for the two groups. Cases that experienced an arrest are designated by their
inclusion in the continuous curve (i.e., continuous line), cases that were not arrested are censored
and are represented as pluses. The strength of this analysis is the inclusion of censored cases.
They are included with the time value computed as the time from release until the last day of
data collection (i.e., June 15, 2007).
The curves reflect several facts: (1) all cases are censored if their time at risk exceeds
2358 days regardless of whether they were arrested or not, (2) 60 percent of post-implementation
cases compared to 50 percent of pre-implementation cases survive (i.e., have not been arrested)3,
and therefore visually demonstrating the cohort difference in overall re-offending, and (3) the
curves, while diverging a small amount, are proportionally similar across time at risk, thus
reflecting no significant difference in the failure rate (confirmed by statistical tests, including the
log-rank test).
Figure 7. Survival (days to re-arrest) of Pre and Post Implementation Groups

Assuming that supervision and surveillance practices are equivalent and that the individual does not indeed commit
any crimes.
3
These are the same percentages in reverse (100%-%arrested) as those displayed in Table 1
This document is a research report submitted to the U.S. Department of Justice. This report has not
been published by the Department. Opinions or points of view expressed are those of the author(s)
and do not necessarily reflect the official position or policies of the U.S. Department of Justice.

MEGAN’S LAW

Survival Functions

Was offender released after
Megan's Law
implementation (after
1995)?

1.0

No
Yes

0.8

No-censored

Cum Survival

Yes-censored

0.6

0.4

0.2

0.0

2000

4000

6000

Days

Reduced Harm by Deterring Sexual Re-offending
Re-arrests for sexual offenses do not significantly differ year to year (see Figure 8).
Holding time at risk constant, 9 percent of the sample has been re-arrested for a sex crime,
representing about 19 percent of the arrest charges. This varies from a high of 14 percent in
1991 and 1992 to a low of 6 percent in 1994, 1995, and 2000.
Pre- and post-implementation groups do not differ in the percent of persons re-arrested
for a sex crime (10% vs. 7.6%). Of the 484 cases represented in the sexual re-offense type
analysis, 60 percent were charged with child molestation or incest, 16 percent with rape and 24
percent with another type of sex offense, including voyeurism and exhibitionism. The pre- and
post-implementation groups also did not differ significantly on sex offense type.
Figure 8. Re-arrest for Sex Offense by Year

Allowing time at risk to include the full period of time from release, only 62 individuals were re-arrested on a sex
charge: 13.2% of the pre-implementation group with between 4,565 and 6,386 days at risk and 9.7% of the postimplementation group with between 2,374 and 4,561 days at risk.
This document is a research report submitted to the U.S. Department of Justice. This report has not
been published by the Department. Opinions or points of view expressed are those of the author(s)
and do not necessarily reflect the official position or policies of the U.S. Department of Justice.

MEGAN’S LAW

60
50
40
30

% Sex Offense

20
10
0
90

91 92

95 96

98 99

Year

As a side note, the percentage of violent crimes, excluding sex crimes, was also
investigated. Overall, 28 percent of the sample was re-arrested for at least one violent crime.
Importantly, only 21 percent of persons released after Megan’s Law was implemented were rearrested for a violent crime compared to 32 percent of the pre-implementation cohort.
PHASE THREE: COST STUDY
Methodology
The final stage of this research grant proved to be the most challenging, as delineating
costs associated with community registration and notification were difficult to disentangle from
other state and county level spending. The research team mailed a cost assessment questionnaire
to the Megan’s Law Units housed within each of the 21 county prosecutor’s office. Megan’s Law
Units are responsible for the enforcement and administration of community notification and
registration statutes in New Jersey (i.e., Megan’s Law). Examples of functions performed by
Megan’s Law Unit personnel include risk assessment (i.e., tier classification), door to
door/community notifications, trainings (e.g., law enforcement, day care center employees),
prosecution/litigation, internet registry maintenance, etc.

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Prior to mailing the cost assessment questionnaires, the research team met with Assistant
Prosecutors in order to review questions contained in the survey and to address any questions
prosecutors may have had in completing the survey. Survey questions were subsumed under two
general categories: start up costs and ongoing yearly implementation costs.
Specifically, startup costs include those initial costs associated with the establishment of
each county’s Megan’s Law Unit. Three variables were included under startup costs:
establishment of the internet sex offender registry, equipment costs, and other/miscellaneous
costs (e.g. computer software). Ongoing costs consist of expenses such as staff salaries, internet
registry maintenance, equipment maintenance/supplies, and other/miscellaneous expenses (e.g.
mailings, printings, software updates, etc.). Survey questions concerning on-going expenses
pertained to costs accumulated during the calendar year ending 2006.
In addition, a section concerning percentage of time allotted to job tasks (i.e. itemized
according to staff title) was included and was to be completed for all staff working within each
county’s Megan’s Law unit. For example, if an investigator was included under personnel, a
percentage breakdown of time allotted to specific job functions such as risk assessment, door to
door notifications, training, etc. was required.
Of the 21 counties that were surveyed, 15 surveys were completed and received by the
research unit, for a total response rate of 71.4 percent. Upon receipt, researchers scanned survey
responses for possible misreading/interpretation issues related to specific survey items. For
additional clarification, researchers called county prosecutor offices to confirm questionable
survey item responses and made any changes accordingly. After survey responses were finalized,
an Excel database of cost assessment variables was created for analysis.

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Along with the cost assessment survey, prior New Jersey state budgets were reviewed for
costs associated with the incarceration, rehabilitation, and tracking of sex offenders.
Specifically, the budgets were searched for any allocation to Megan’s Law. Moreover, original
grant documentation and archived folders were also reviewed for costs not included or found in
the other sources. Sources were challenging to locate, as was the origin of much of the funding.
Results
The results that follow include statistics based on the 15 counties that responded to the
research unit’s Megan’s Law Cost Assessment Survey. For the 15 responding counties, the initial
aggregate implementation cost of Megan’s Law totaled $555,565. Of this total startup cost,
establishment of the internet sex offender registry accounted for $186,190, an average of $31,032
(sd=$24,140) per county, equipment accounted for $232,407 ($19,367 average per county,
sd=$14,212), and other/miscellaneous costs accounted for $136,968 ($12,452 average per
county, sd= $17,702). In addition, total aggregate expenses for all 15 counties attributable to the
ongoing implementation of Megan’s Law were estimated to be $3,973,932 per annum (i.e.,
according to the fifteen participating counties). Of total per annum costs, staffing costs accounts
for $3,605,972 ($257,569 average per county, sd= $160,180), internet sex offender registry
maintenance

accounts

for

$146,300

($20,900

average

per

county,

sd=$20,178),

equipment/supplies accounts for $130,483 ($10,037 average per county, sd= $8,196), and
other/miscellaneous expenses accounts for $91,177 ($6,513 average per county, sd= $6,002).
Additional information gathered from the prosecutor’s surveys includes counts of staff
within each county’s Megan’s Law Unit, number of cases handled per year, and number of door
to door notifications per year. According to completed surveys, the number of employees
dedicated to Megan’s Law Unit operations totals 78 (5.2 average per county, sd= 3.2), and an

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estimated 5,873 Megan’s Law specific cases were processed (391.5 average per county, sd=
303.4). Moreover, counties reported that law enforcement officers performed a total of 31 door–
to-door notification events (3.9 average per county, sd= 2.7) throughout the year (e.g. 1 event
equals 300 households) for tier three sex offenders.
A question concerning ongoing costs for the calendar year ending 2006 was also included
in the survey to measure yearly cost increases/decreases. The cost for Megan’s Law
implementation during calendar year 2006 was estimated to be $1,557,978, whereas
implementation costs during calendar year 2007 totaled $3,973,932 for responding counties5.
This change represents a 155% increase in ongoing expenses from calendar year 2006 to
calendar year 2007. These increases were obtained from raw figures provided by the Megan’s
Law Units and did not reflect specific costs.

However, with the inception of the Global

Positioning Satellites used for Tier 3 sex offenders, it can be surmised that a portion of the
increases can be attributed to increased surveillance. Finally, research of prior state budgets
documented a $200,000 expenditure on Megan’s Law DNA Testing for fiscal years since 2000.
There are no other distinguishable appropriations. Most costs are combined with salaries or
another type of operating expenses.

PROJECT SUMMARY

As noted, fifteen out of a possible twenty one counties in New Jersey responded to the cost assessment survey, which translates to an
approximate response rate of 71.4%. In order to provide a more accurate assessment of initial and on going costs statewide, said costs
were interpolated by adding 28.6% (i.e. 100% - 71.4%) to the implementation and ongoing grand totals of the fifteen responding
counties. . In effect, using this general interpolation method, implementation costs were estimated to be $714,457 and ongoing costs were
estimated to be $5,110,477 statewide. Again, because these figures are interpolated, these costs do not take varying county demographics
into account and should be interpreted with this caveat.
This document is a research report submitted to the U.S. Department of Justice. This report has not
been published by the Department. Opinions or points of view expressed are those of the author(s)
and do not necessarily reflect the official position or policies of the U.S. Department of Justice.

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The three phases of this study were designed to test the effectiveness and cost of Megan’s
Law using multiple methods and strategies. In none of the analyses was Megan’s Law
definitively found to be effective. Since sex crime rates have been down prior to Megan’s Law
and pre and post samples do not indicate statistically lower rates of sexual offending, the high
costs associated with Megan’s Law are called into question.
Summary of Results
As a preliminary step in assessing the effect of community registration and notification
laws on sexual arrest rates in New Jersey, the goal of the trend study was to explore crime trends
and to identify possible changes over a 21-year period. Specifically, the main research areas
concerned the patterns of sexual offense rates both prior and subsequent to the implementation of
Megan’s Law, as well as comparisons in crime rates between sexual, drug, and non-sex/non-drug
based offenses during the same time period. The results presented in this report support findings
by other researchers exploring relevant topics. Most notably, Finkelhor and Jones (2004) found
that there has been a consistent downward trend in child sexual abuses cases since the early
1990s.
This trend analysis did indeed find a significant change in the statewide decreasing sex
offense rate in the year Megan’s Law was implemented, which may lead some readers to believe
that the legislation is solely responsible for the decline. Because sex offense rates began to
decline well before the passage of Megan’s Law, the legislation itself cannot be the cause of the
drop in general. It may, in fact, be the case that continuing reductions in sex offending in New
Jersey, as well as across the nation, are a reflection of greater societal changes. Having said this,
it is nevertheless hard to explain the steeper decline in rates after the implementation of Megan’s
Law. Given that sex offenses are low base rate events, the finding that these rates continue to

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decline at an accelerated rate after 1994 suggests that something other than a natural decline may
be responsible. Although the initial decline cannot be attributed to Megan’s Law, the continued
decline may, in fact, be related in some way to registration and notification activities. However,
there may well be additional factors causing this steeper rate of decline after 1994, perhaps some
attributable to other public policies. For example, in 1998, New Jersey began civilly committing
those sex offenders found to present the highest risk to the community, termed sexually violent
predators. Assuming the accuracy of the risk assessment that underlies the civil commitment of
these sexually violent predators, then those at highest risk to reoffend have been removed from
the community, thereby potentially lowering the sex offense rate. Although, the number of
civilly committed sexual predators only includes approximately 350 sex offenders.
Moreover, this statewide finding of a declining sex offense rate should be taken with
considerable caution. The variation in the pre-post-implementation rate trends at the county
level suggests that the statewide effect may be an artifact of the aggregation process. Although
many counties (9 of 21) follow the state trend, many others show no differences in rates over
time or have experienced reductions followed by increases to near pre-Megan’s Law levels. Even
so, with only two exceptions, the rates of sex offending were highest prior to 1994 and lowest
after 1995, with the most recent years having the lowest rates. Differences in population, sociopolitical status, policing and prosecutorial resources may be related to differences in the
effectiveness in notification and surveillance activities in specific counties.
Although impossible to distinguish the nature of the effects, the reductions of sex
offenses is related to some historical process: either (1) registry/notification, surveillance and/or
aggressive prosecution under a more mature Megan’s Law is responsible for the continued

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reductions or (2) general public awareness, publicity, and/or exclusion and intolerance feed the
continued decline. Most likely, it is a combination of these factors.
In the offender release sample, there is a consistent downward trend in re-arrests,
reconvictions and re-incarcerations over time similar to that observed in the trend study, except
in 1995, when all measures spiked to a high for that period. This resulted in significant
differences between cohorts (i.e., those released prior to and after Megan’s Law was
implemented). Similarly, re-arrests for violent crime (whether sexual or not) also declined
steadily over the same period resulting in a significant difference between cohorts (i.e., those
released prior to and after Megan’s Law was implemented). However, because these trends
began before Megan’s Law was passed, this decline cannot be attributed solely to Megan’s Law
activities.
In all other pre-post measures, including other measures of recidivism, community tenure
and harm reduction (decreased sexual offending), no significant differences between cohorts
were found. As such, Megan’s Law does not illustrate effectiveness in:
o increasing community tenure (the time spent in the community prior to re-arrest);
o reducing sexual re-offenses;
o changing the type of sexual re-offense or first time sexual offense (for example,
from hands-on to hands-off offenses); or
o reducing the number of victims involved in sexual offenses.
Costs associated with the initial implementation of Megan’s Law, as well as ongoing
expenditures, continue to grow over time. Start up costs totaled $555,565 in 1994 and now
current costs (in 2007) total approximately 3.9 million dollars. Given the lack of demonstrated
effect of Megan’s Law, the researchers are hard-pressed to determine that the escalating costs are
justifiable.
Limitations

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Conducting a study of this type with sensitive sexual arrest data introduces a number of
limitations. The most noted problem plaguing sexual offense research, the low base rate of
reported sexual offenses, is tied to the under-representation of official data. Because sexual
offenses are under-reported, most measures of recidivism under-represent the true offending
rates (American Psychiatric Association [APA], 1999; Belknap, 2000; Furby et al., 1989; Hall,
1995; Hanson & Bussiere, 1998). It has been suggested that the present statistics on sexual
abuse represent approximately one-third of the number of actual victimizations, leaving
researchers and practitioners concerned about the “dark figure” of sexual abuse (APA, 1999;
Belknap, 2000; Chesney- Lind, 1997). Legal definitions, fear and shame, and a desire for
privacy are the main contributors to the unwillingness of many victims to report their abuse.
Conversely, it has been noted that some types of sexual abuse may be over-represented to the
police, such as stranger rapes (Belknap, 2000).

For example, victims of stranger rape, as

opposed to incest victims, may be more inclined to report their sexual victimization because their
perpetrator is unknown. This disparity may lead many to believe that stranger victimizations
occur more frequently than other types of sexual victimizations because the reports may appear
disproportionately higher (Zgoba & Simon, 2004).

Although most individuals know that

acquaintance or familial crimes are more frequent, these factors may make it difficult to achieve
a clear picture of sexual offense rates (Belknap, 2000; Chesney- Lind, 1997). Given this low base
rate of reporting, it is notable that sex offenses decrease rapidly in the post-Megan’s law period;
the fact that the decrease accelerates as the number of crimes decreases is unexpected.
Another issue that has been difficult to fully address in the format of this study is whether
the noted decreases in the post-Megan’s law period can be attributed to specific deterrence or a
more general deterrent effect. The intent of Megan’s law was to reduce repeat arrests among

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known sex offenders. That is, Megan’s law was designed as a specific deterrent. However, the
idea of notification and increased surveillance may have a general deterrent effect. Further,
increased attention and public contempt of sex offenses and offenders may also contribute to
general deterrence. This study illustrated downward trends in sexual arrest rates, but cannot
differentiate whether the reduction is due to decreases in new first-time sex offenses (general
deterrence) or to decreases in sexual re-offenses (specific deterrence).
One of the largest challenges, and a subsequent limitation, associated with this grant was
obtaining the financial costs regarding Megan’s Law. County Prosecutor Offices, as well as the
offices dealing with Treasury and Budget, had the same difficulties the researchers experienced
when attempting to isolate and identify the costs listed in the State of New Jersey Budgets.
Furthermore, initial start-up costs were sometimes funded through grants that providing few
specifics regarding disbursement patterns. In an effort to provide close estimates, the researchers
developed proxy measures that should be read with some caution.
Conclusion
Despite wide community support for these laws, there is little evidence to date, including
this study, to support a claim that Megan’s Law is effective in reducing either new first-time sex
offenses or sexual re-offenses. Continuing research should focus on matching samples of sex
offenders before and after the implementation of Megan’s Law and also examining levels of
supervision associated with Megan’s Law. Further research will be conducted utilizing the data
accumulated here, specifically exploring low base rate offending and potential predictors of
sexual recidivism. Should future studies establish that Megan’s Law has no demonstrable effect
on the rates of sexual offending, policy makers and legislative leaders should investigate other

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options for lowering sex offense rates, such as mandated treatment of all sex offenders, potential
use of polygraph testing and intensive probation and parole supervision.

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