Catch Me If You Can

Registered Sex Offenders in the United States

Twice per year, the National Center for Missing and Exploited Children (NCMEC) conducts a phone survey of state registry officials and publishes a jurisdictional count of registered sex offenders (RSO) portrayed on a map. The map, published on the Center's website, depicts an aggregate count of RSOs in each state and territory, along with U.S. census-adjusted figures calculating the rate of RSOs per 100,000 citizens. The count from July 2012 reported that there were 722,499 RSOs living throughout the United States and its territories, with an average rate of 231 RSOs per 100,000 people in the population. Given the dearth of published data describing the national sex offender population, NCMEC’s map has become a ubiquitous source of information for the media and policymakers, providing a snapshot of the RSO population in each state and nationwide.

Studying the sex offender population in an effort to paint a more illustrative national portrait has proven challenging because registries are independently administered by states. With no integrated data system, states use widely varying definitions, terminology, and data reporting procedures, which creates challenges for researchers (Harris, Levenson, & Ackerman, 2012). Recently, however, using records downloaded directly from public registries (about two thirds of the total RSO population), researchers were able to consolidate data and draw more detailed inferences about American sex offenders (Ackerman, Harris, Levenson, & Zgoba, 2011). RSOs are overwhelmingly male (98%) and predominantly White (66%). Blacks are over represented on registries compared to estimates that they comprise less than 13% of the U.S. population (U.S. Census Bureau, 2010), which is not unexpected, since Blacks and minorities are over represented in criminal justice populations for a complex array of reasons (Miller, 1996). Individuals of all ages can be found on state registries, but the average American sex offender is currently in his mid-40s (Ackerman et al., 2011).

Approximately two thirds of RSOs counted by NCMEC are found on public registries (Ackerman et al., 2011). Presumably, that means that about one third of the nation's sex offenders have been assessed by their state’s sex offender management procedure to pose low risk for sexual recidivism and therefore are not eligible for community notification. Many states list all RSOs on public websites regardless of risk, but about 14% of RSOs on public registries have been specifically designated by states as high risk, predator, or sexually violent. About 15% have more than one sex crime conviction. Approximately 90% have had a minor victim, and about 33% have had victims below 10 years of age. Most (87%) victims (adult and minor) are female (Ackerman et al., 2011).

Notably, a substantial number of RSOs do not reside in the community (Ackerman et al., 2011; Ackerman, Levenson, & Harris, 2012; Harris et al., 2012). It is estimated that on any given day as many as 18% are double-counted in more than one state, and about one in five is incarcerated (Ackerman et al., 2012). Deceased and deported RSOs remain on registries as well and account for about 4% of the population. One particular anomaly is Florida, where more than half of all registrants are living out of state or confined. Without considering these adjustments, state rates reported by NCMEC are vulnerable to inflation. When those not residing in the community were accounted for, RSO counts and rates were reduced considerably—in Florida, by more than half (Ackerman et al., 2012).

FTR and Sexual Recidivism

The risk of sexual reoffending by previously convicted sex offenders is a legitimate cause for public concern. SORN laws attempt to prevent recidivism by increasing scrutiny of sex offenders through enhanced law enforcement monitoring and public awareness. Although high recidivism rates are often cited as a rationale for SORN laws, sexual offense recidivism rates are lower than commonly believed. Sex crimes often go unreported, however, and therefore official arrest data underestimate actual reoffending. Sexual recidivism rates range from 5.3% over a 3-year period (Bureau of Justice Statistics, 2003) to approximately 14% over 4 to 6 years (Hanson & Bussiere, 1998; Hanson & Morton-Bourgon, 2005) and about 24% over 15 years (Harris & Hanson, 2004). Sex offenders recommit similar crimes at lower rates than property offenders and drug offenders (Bureau of Justice Statistics, 2002; Sample & Bray, 2006). The likelihood of sexual recidivism increases with the presence of risk factors such as prior sexual and nonsexual arrests, unrelated and male victims, and younger offender age (Hanson & Bussiere, 1998; Hanson & Morton-Bourgon, 2005).

Only a handful of published studies have specifically focused on the relationship between FTR and sexual recidivism. In general, empirical data from Minnesota, New Jersey, New York, and South Carolina have not supported the hypothesis that sex offenders who fail to register are more sexually dangerous than their compliant counterparts (Duwe & Donnay, 2010; Levenson, Letourneau, Armstrong, & Zgoba, 2010; Levenson, Sandler, & Freeman, 2012; Zgoba & Levenson, 2012). Common findings across studies suggest that FTR is not significantly associated with sexual recidivism, though it is associated with nonsexual recidivism, and that FTR occurs in combination with a history of prior crimes and versatility in criminal offending. Registration noncompliance and sexual reoffending may characterize different risk constructs, with FTR being related to rule breaking behavior and sexual reoffending being related to sexual deviance. Researchers in Washington found that most new convictions were for general criminal and violent activity (38.5% and 15.8%, respectively); with each additional FTR conviction, the likelihood of sexual recidivism declined, but the likelihood of general felony recidivism increased (Washington State Institute for Public Policy, 2006).

In Minnesota an arrest for FTR did not predict sexual or general recidivism, but Duwe and Donnay (2010) found that FTR offenders were less educated, less likely to have participated in treatment, less violent, less likely to have assaulted victims of different age groups, more likely to be a racial minority, and more likely to have prior felonies and supervision violations. In South Carolina, FTR offenders were younger, had more prior nonsexual arrests, were more likely to be non-White, and were less likely to have a minor victim, but they were not more likely to sexually recidivate (Levenson et al., 2010). A similar study in New Jersey concurred that FTR was not a significant predictor of sexual recidivism; FTR offenders were more likely to have sexually assaulted a stranger and to have adult female victims, refuting the myth of the predatory pedophile who absconds in order to avoid detection (Zgoba & Levenson, 2012). In New York, FTR offenders were younger, more likely to be a minority race, and they had more extensive criminal histories and more breaches of supervision (Levenson et al., 2012). Parole or probation supervision decreased the odds of a FTR charge and nonsexual recidivism, suggesting that supervision officers apparently assisted offenders to remain compliant with registration duties and also seemed to help offenders resist general criminal behavior (Levenson et al., 2012).

It is commonly assumed that “sex offenders often fail to register precisely so they can evade detection and in many cases, find new victims. . . ” (Blumenthal, 2011, p. 1). Recent enhancements to penalties for registration noncompliance further imply that sex offenders who fail to register are believed to pose an increased risk for sexual reoffending. Indeed, the literature points to criminality and self-regulation deficits as robust predictors of both sexual and nonsexual recidivism (Hanson & Morton-Bourgon, 2005). It would, therefore, be reasonable to hypothesize that FTR reflects an antisocial rule-violating orientation and a resistance to authority, which could increase the threat of subsequent sexual and nonsexual criminal behavior.

Alternatively, it is possible that in some cases FTR results from the complexities of the registration process itself and the reintegration obstacles posed by community notification. Duwe and Donnay (2010) found that lower education was associated with a greater likelihood of FTR, suggesting that complicated reporting requirements may be challenging for offenders with lower intellectual functioning. SORN laws have the potential to impede community reentry by disrupting employment, housing, and prosocial relationships (Levenson & Cotter, 2005; Levenson, D’Amora, & Hern, 2007; Mercado, Alvarez, & Levenson, 2008; Tewksbury, 2005), all of which may undermine criminal desistance and contribute to difficulties complying with registration mandates.

FTR and the Problem of “Missing” Sex Offenders

While media portrayals often conflate FTR with absconding from authorities, scholars have noted that FTR is not necessarily tantamount to absconding (Harris & Pattavina, 2009; Levenson & Harris, 2012). It is difficult to specifically confirm the number of fugitive sex offenders, given that states have widely disparate methods for classifying or distinguishing absconders, registration violators, and others whose locations are uncertain. It is commonly reported that 100,000 RSOs are “missing” (Parents for Megan’s Law, 2010; Reauthorization of the Adam Walsh Act, 2011; U.S. Marshals Service, 2007). When analyzing data downloaded from public registries, however, researchers found no empirical evidence to support the notion that 100,000 of the nation’s sex offenders are unaccounted for; about 3% of RSOs are labeled as noncompliant or their whereabouts are unknown, and an additional 2% of the nation's sex offenders may be transient or homeless without a specific address (Levenson & Harris, 2012). Rates of noncompliance vary widely across the states, with a median rate of 2.7%, and Florida is consistently reported to have one of the lowest registration violation rates in the country. The most inclusive estimates revealed that the addresses of approximately 22,000 to 36,000 RSOs may be unverified (Levenson & Harris, 2012).

It is also doubtful that all sex offenders arrested for FTR are willful violators, as most FTR offenders are easily located and do not appear to have absconded (Duwe & Donnay, 2010; Harris et al., 2012; Levenson et al., 2010; Levenson et al., 2012; Zgoba & Levenson, 2012). Some sex offenders may appear to be “missing” due to administrative errors, inadequate or incomplete address information, data entry anomalies, lag times in updating registry information, unauthorized travel, or homelessness (Harris & Pattavina, 2009). Some offenders may carelessly disregard their duty to update registration information, but most remain in their known locations despite their lapse. Other sex offenders, however, might indeed be inclined to avoid the stigma and collateral consequences of sex offender registration and purposely abscond.

Nationally, about 10% of probationers and parolees in the United States have absconded (Bureau of Justice Statistics, 2007), but sex offenders are less likely to abscond than nonsex offenders (Grattet, Petersilia, Lin, & Beckman, 2009; Williams, McShane, & Dolny, 2000). While absconding is defined differently in each state, it typically refers to an individual under community supervision who has failed to inform his parole or probation officer of his whereabouts and cannot be located. Research on parole fugitives suggests that many flee due to a perceived inability to comply with an overwhelming, complex, and rigid set of rules (Schwaner, McGaughey, & Tewksbury, 1998). Absconders are more likely than nonabsconders to have a history of probation failure, revocation, and fleeing supervision (Schwaner, 1997). Most abscond within the 1st year and seem to fit into one of five predominant categories: socially or psychologically impaired, drug addicts or dealers, career criminals and rule violators, impulsive risk-takers who may unwittingly commit a violation or a new crime, and first time offenders who, unaccustomed to the restrictions of parole, violate release conditions in order to preserve their families (Schwaner et al., 1998).

Parole absconders have not necessarily been found to be a high-risk criminal group and the two most common reasons for absconding were drug relapse or a technical rule violation (Schwaner et al., 1998). Williams and colleagues (2000) found that those most likely to abscond were drug offenders, previous parole violators, and those with unstable employment or housing. Prior arrests and being unmarried also predicted absconding, and, consistent with Schwaner's findings, the authors noted that “absconders tend to be less dangerous and less risky parolees” (Williams et al., p. 36). In stark contrast to the emphasis placed on tracking sex offenders, little effort is expended in many states to locate fugitive parolees (Williams et al., 2000).

It is well established that many RSOs experience unemployment, housing disruption, harassment, and social alienation as a result of SORN laws (Levenson & Cotter, 2005; Levenson et al., 2007; Mercado et al., 2008; Tewksbury, 2005; Tewksbury & Lees, 2006; Zevitz & Farkas, 2000). Likewise, many RSOs report depression and hopelessness (Jeglic, Mercado, & Levenson, 2011). The multitude of restrictions, along with lengthy durations of registration, may cause some sex offenders to desperately flee with hopes of resuming a “normal” life. There may be a variety of reasons why some sex offenders fail to comply, but the question remains whether RSO absconders are actually at increased risk for sexual recidivism.

Data

Data for this study were obtained from two sources. The first data file, provided by the Florida Department of Law Enforcement (FDLE), included publicly available information regarding all RSOs on the Florida sex offender registry, as well each offender's last registration date and registration requirements (biannual or quarterly). This file was obtained in April 2011, but did not include offense data. In order to obtain information about the offender's sex crime convictions, a second data file was obtained via an automated data-scraping process known as a spider-scrape. This method allows for the automated download of all relevant information on a given website. In January 2011, the researchers worked with a computer programmer to obtain a data file with all available information on the Florida sex offender registry. The file obtained from FDLE included 55,848 offenders, and the scraped data included 55,942 offenders.

Offenders in each file were matched by last name, first name, and birth date. This process allowed the researchers to merge the two files into one data set that included all the variables provided by FDLE and the offense data provided by the scrape. The offense data was linked to the offender by a unique identifier created with the scraped data file. As such, each row of the offense data contained one specific offense and the full file included 83,699 offenses. Given our interest in the number of sex crime conviction dates (rather than the types of charges) and because an offender can incur multiple charges for one crime event, when an offender had multiple sex crime convictions on one adjudication date, only one charge was kept.

The offense level data file captured each individual offense as a row, whereas the offender file identified each row as one specific offender. As such, the offense level data was aggregated to the offender level and then matched and entered into the offender level file. Offenders with no offense data (n = 110) and those with an FTR listed as their first offense (n = 17) were deleted from the data set, providing a total population of N = 55,721.

Participants

Because we were interested in investigating registration compliance behavior while at-large, we created a subpopulation of all those offenders who were residing in the community in April 2011. All offenders who were listed as living out of state (n = 12,793), deported (n = 2,024), deceased (n = 445), civilly committed (n = 580), or otherwise in custody (n = 16,302) were filtered out and the subpopulation under study included only those individuals living in the community in Florida (n = 23,557). Characteristics of the full RSO population and community subpopulation can be seen in Table 1.

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Table 1.

Descriptive Statistics.

	RSO Living in Community N = 23,557	RSO Population N = 55,721
	Mean / %	Mean / %
Offender	90%	84%
Predator	10%	16%
AWA Tier 2 (biannual)	69%	63%
AWA Tier 3 (quarterly)	31%	37%
Race (minority)	25%	26%
On supervision*	25.4%	13%
Gender (male)	97.4%	98.1%
Age at conviction	35	34
Minor victim	81%	77%
More than one sex crime conviction	5.4%	9.5%
Ever convicted of FTR	11.5%	8%
Transient	3.3%	1.4%
Noncompliant	6.2%	N/A
1 to 3 months	1%	N/A
3 to 6 months	1%	N/A
6 months to 1 year	1%	N/A
> 1 year	3%	N/A
Listed as absconded	3%	1.3%
Absconded from probation	1.8%	0.75%
Absconded from registration	1.2%	0.56%
Age at noncompliance	45	N/A
Days noncompliant	46	N/A
No FTR convictions	89%	92%
One FTR conviction	10%	7%
Two or more FTR convictions	1.6%	1.3%

*

DOC Probation, DOC community control, DOC administrative probation, Parole, or Federal Probation.

Variables

Several variables were readily available in the FDLE file. Other key variables were created or calculated based on data available from the FDLE file. These items are explained in more detail below.

Analyses

First, frequencies and means were calculated to provide descriptive statistics. Next, various group comparisons were conducted using χ² analyses for categorical independent variables. Finally, t-tests were utilized to examine group differences for continuous independent variables. Analyses were conducted using SPSS version 20.

Descriptive Statistics

The subpopulation of all RSOs living in the community in Florida in April of 2011 consisted of 23,557 individuals, and their characteristics can be seen in Table 1. One in 10 RSOs living in the community was designated a predator, and fewer than one third fell into the AWA Tier 3 (highest risk) group. Most were White, though minorities were over represented. Slightly more than one quarter of those living in the community were under some sort of correctional supervision. Less than 3% were female, and the average sex offender was convicted while in his mid-30s (median = 33, mode = 19). About four out of five have had a minor victim, and only 5.4% met our definition for “repeat sex offender” with more than one sex crime conviction.

About 6% were currently noncompliant with registration mandates, with 11% having been convicted at some time for FTR and 3% officially listed as absconded. An individual can be out of compliance for failing to update his registration during the specified month, but the FDLE defines absconders as those who no longer reside at the last reported address given to the Florida Sexual Offender Registry. It should be noted that an offender can be out of compliance or absconded but not (yet) have a FTR conviction. Of those documented as out of compliance, about half were noncompliant for over 1 year. The average number of days noncompliant was 46, but the median and mode were zero (range 0-4885). Less than 2% of the offenders had multiple convictions for FTR.

As reflected in Table 1, the RSOs living in the community appear to be somewhat lower risk than the full RSO population (which makes sense because the full population includes those who have been subsequently returned to prison for a new offense since their initial registration). For instance, the community group had a lower proportion of predators, AWA Tier 3 offenders, and repeat offenders (presumably because many of the recidivists are confined). The community group had a slightly higher proportion with minor victims.

Group Comparisons

To test the hypothesis that RSOs who display varying degrees of registration noncompliance are more sexually dangerous to the community in general and to children in particular, we examined three dichotomous variables designating whether the RSO was currently out of compliance with registration, had ever been convicted for FTR, or was designated as absconded. These groups were not mutually exclusive; in other words, a particular offender could be coded as “1 = yes” on more than one of these variables. Differences within each type of violator grouping were then examined for significant differences in specific demographics and characteristics associated with reoffense risk (see Table 2).

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Table 2.

Group Comparisons of Violators and Nonviolators.

RSO Living in CommunityN = 23,557	Group Comparisons
IV	% of Noncompliant N = 786	% of Compliant N = 22,099	χ2	% of FTR Conviction N = 20,854	% of No FTR N = 2,793	χ2	% of Absconded N = 690	% of Not Absconded N = 22,867	χ2
Predator	6.5	9.7	8.504**	5.7	10	50.610***	6.7	9.6	6.501*
AWA Tier 3	31.5	30.5	0.368	29	30.8	3.689	33.5	30.5	2.760
Race (minority)	32.8	24.2	29.945***	38.3	22.7	313.813***	25.7	24.5	0.508
Gender (male)	99.1	97.3	9.057**	97.5	97.4	0.066	98.3	97.4	2.038
Minor victim	77	81.2	8.751**	74.5	81.6	78.667***	71.9	81.1	36.380***
On supervision	30.2	26	6.943**	22.4	25.8	14.735***	N/A: see Table 3
Repeat sex offender	9.8	5.3	28.504***	4.9	5.4	1.494	2.8	5.5	9.647**
Transient	20.4	2.8	713.348***	3.2	3.3	0.160	1.6	3.4	6.545**
Comparison of mean age
	Noncompliant	Compliant	t-test	FTR	NO FTR	t-test	Absconded	Not absconded	t-test
Age at Conviction	35.4	34.8	−1.278	30.4	35.4	19.384***	33.4	34.8	2.942**

*

p < .05. **p < .01. ***p < .001.

Noncompliants had significantly greater proportions (compared to compliants) of predators, minorities, males, RSOs under probationary supervision, repeat offenders, and transients, but had significantly fewer RSOs with minor victims. Significant differences were not found for AWA tiers. Those with a conviction for FTR were significantly more likely to be of a minority race, but less likely than those without FTR convictions to be a predator, to be on supervision, or to have a minor victim. Absconders were significantly less likely to be a predator, to have a minor victim, to be a repeat sex offender, to be on supervision, or to be transient. Compliants and noncompliants did not differ significantly on age, but convicted FTR offenders and absconders were significantly younger than nonviolators.

We then created mutually exclusive categories to examine differences between those who had absconded from probation and those who had absconded from registration (see Table 3). Probation absconders were more likely to be predators and AWA Tier 3, but registration absconders were more likely to be of a racial minority, to be transient, and to have had a conviction for FTR. We also created mutually exclusive categories to compare compliants, noncompliants, and absconders with each other to determine if significant differences existed between groups (see Table 3). Compliants had the highest proportion of predators and those with minor victims. Noncompliants had the highest proportion of racial minorities, males, repeat sex offenders, transients, and those convicted of FTR. As a group, absconders were the least likely to have minor victims, to be a repeat sex offender, to be transient, or to have been convicted of FTR. Transients were more likely to abscond from registration than probation.

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Table 3.

Mutually Exclusive Group Comparisons.

RSO Living in Community N = 23,557	Group Comparisons
IV	% of Absconded From Probation n = 401	% of Absconded From Registration N = 277	χ2	% of Compliant N = 22,099	% of Noncompliant N = 768	% of Absconded N = 690	χ2
Predator	9.7	2.5	13.424***	9.7	6.5	6.7	15.073**
AWA Tier 3	38.7	25.6	12.501***	30.5	31.5	33.5	3.127
Race (minority)	21.7	30.7	6.994**	24.2	32.8	25.7	30.424***
Gender (male)	99	97.5	2.401	97.3	99.1	98.3	11.182**
Minor victim	71.8	72.6	0.45	81.2	77	71.9	45.037***
Repeat sex offender	2.2	3.2	0.640	5.3	9.8	2.8	38.547***
Transient	0.5	3.2	7.764**	2.8	20.4	1.6	730.666***
Convicted of FTR	6.7	13.4*	8.410**	11.3	17.1	10	25.520***

*

p < .05. **p < .01. ***p < .001.

Table 4 shows mean differences in the number of FTR convictions and number of days noncompliant for relevant demographic and risk-related variable groups. As can be seen by the standard deviations, there was wide variance in all cases. For the FTR convictions, it is important to note that 89% of the community population had no FTRs (n = 20,854); 2,313 individuals had one FTR conviction, 339 had two, 47 had three, and four RSOs had four FTR convictions. Significantly lower mean numbers of FTR convictions were found for predators, AWA Tier 3, Whites, those on supervision, and those with minor victims. The number of days noncompliant also had a wide variance, with a range of 0 to 4885 (13 years); about 2.7% had more than 1,000 days noncompliant. However, 85% were completely compliant, 90% were within 14 days, and 6% had more than 30 days. As described previously in the method section, because Florida allows RSOs to register any day within their prescribed month, anyone within 30 days was considered compliant for our purposes. Significantly fewer days noncompliant were found for predators, those on supervision, females, and those with minor victims. Transients had a much higher mean number of days out of compliance with registration mandates.

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Table 4.

Comparison of means: FTR and Days Noncompliant.

RSO Living in Community N = 23,557
	# FTR Convictions Range = 0 to 4	(SD)	t-Test	# Days Noncompliant Range = 0 to 4885	SD	t-Test
Predator	.08	(.30)		33.2	(246)
Offender	.14	(.41)	9.121***	47.6	(295)	2.576*
AWA Tier 3	.14	(.40)		51.9	(282)
AWA Tier 2	.13	(.30)	2.153*	43.8	(310)	−1.898
Race (Minority)	.21	(.49)		46.5	(277)
Race (White)	.11	(.36)	−14.942***	45.5	(295)	0.210
On Supervision	.12	(.38)		14.1	(97)
No Supervision	.14	(.41)	3.563***	55.2	(331)	15.380***
Gender (male)	.13	(.39)		46.9	(293)
Gender (female)	.12	(.36)	0.671	20.8	(153)	4.011***
Minor victim	.12	(.38)		42.8	(288)
Adult victim	.18	(.46)	7.893***	60.5	(302)	3.551***
Repeat sex offender	.12	(.38)		39.2	(214)
First-time offender	.13	(.40)	1.169	46.6	(295)	1.179
Transient	.13	(.42)		99.5	(272)
Stable Address	.13	(.40)	−0.070	44.4	(291)	−5.541***

*

p < .05. **p < .01. ***p < .001.

Limitations

This research was restricted by the nature of secondary data, in which variables are sometimes defined in ways that are imperfect for the researcher. For instance, we did not have true recidivism data, but rather we created a proxy called “repeat offender” which was defined as more than one sex crime conviction listed on the registry. As well, our data set did not contain nonsexual criminal history, so we were unable to examine registration violation in relation to nonsexual criminal tendencies or nonsexual reoffending. Future studies should certainly attempt to analyze these factors, given the strong indications that both FTR and absconding may be more strongly associated with general criminality than sexual deviance. Limited information about offender and victim characteristics restricted our ability to utilize empirically derived risk assessment instruments in our analyses, and we were therefore restricted to using only the offense-based risk designations provided by Florida's predator and AWA status. We acknowledge that these tier assignments are based on statutes of conviction and therefore are not an empirically derived assessment of risk. This exploratory study was intended to be primarily descriptive and comparative, and our objective was simply to facilitate a more informed understanding of registration violation across the spectrum of that construct.

Implications for Policy, Practice, and Research

We suggest that registration noncompliance should be viewed on a continuum that reflects a range of behaviors such as inadvertent noncompliance, confusion about requirements, poor self-management skills, a tendency toward rule violation, or outright rebellion against registration mandates. Policymakers might note that absconders may not be the stereotypical pedophilic predators one might expect, and that a host of explanatory factors might influence absconding behavior. Harris et al. (2012) found that few state registries clearly distinguished sex offenders confirmed to have absconded, and that reports of “missing” sex offenders often included those flagged as delinquent or technically out of compliance with registration mandates. States should consider refining their nomenclature to reduce definitional ambiguity and improve the ability to distinguish true absconders from other types of registration violators. Such modifications might provide richer opportunities for research and allow for a more nuanced and tailored approach to legal sanctions and clinical or supervisory interventions.

If a goal is to increase registration compliance so that law enforcement agents and the public can maintain awareness of sex offender locations, then further research should focus on efforts to uncover and understand the myriad causes of absconding. Once those factors are identified and better understood, mechanisms might be put in place to improve compliance rates. As well, policymakers should note that transients are more likely to abscond from registration after the termination of their probation, undermining the very objective of sex offender registries. If residence restrictions are a leading cause of transience, and transience leads to absconding, legislators might want to reconsider laws that contribute to sex offender homelessness.

These findings could assist community correctional officers and treatment providers to work with clients on identifying psychosocial stressors that might challenge their coping skills and lead to registration failures. Helping clients anticipate and cope with obstacles to reintegration might facilitate better prevention of supervision and registration failures. In an effort to prevent absconding, treatment providers should continually assess dynamic risk factors which might lead to despondence and desperate coping measures by sex offenders who are experiencing reentry barriers.

Future research should investigate factors related to registration noncompliance and absconding in other states to determine whether these results can be replicated and generalized or if the findings are specific only to Florida. Further research utilizing predictive modeling is planned to identify factors that influence sex offender absconding, which may assist lawmakers and practitioners to fashion appropriate legal and psychological interventions. Future investigations should also include qualitative interviews or surveys of registration violators to attempt to uncover and better understand the array of underlying motivations.