Criminal Recidivism of Incarcerated Male Nonviolent Offenders in Hong Kong

Abstract

Criminal recidivism of the incarcerated population in Hong Kong has rarely been studied. The purpose of this study is to explore the recidivism rates and to identify significant predictors of reoffending among incarcerated male offenders convicted of a nonviolent offense in Hong Kong. Using a self-reported methodological design, 278 offenders were sampled. These offenders’ immediate past incarceration is used as the benchmark for this recidivism study. The 1-, 2-, and 3-year recidivism rates are 21%, 68%, and 87%, respectively. The findings denote that offending history, psychological attributes, interpersonal relationships, and environmental influences are significant reoffending risk factors. These findings, especially the alarming failure rates, highlight the need to seriously assess the effectiveness of intervention strategies used by the Hong Kong correctional system in preventing future offending. Implications for intervention strategies with emphasis on the risk factors for recidivism are discussed.

Keywords

recidivism reoffending risk factors nonviolent offenders reincarceration Hong Kong Chinese

Introduction

Increasing attention has been placed on issues related to offenders’ propensity for reoffending, specifically with emphasis on criminal desistance (see, for example, Farrall & Calverley, 2006; Laub & Sampson, 2001). This criminological issue about offending, including nonviolent offenses, is also an important one in Hong Kong. According to the official statistics, 62,836 nonviolent offenses were reported in 2011, most of them (56%) being theft-related offenses (Hong Kong Police Force, 2012). This trend has been relatively consistent over the past 5 years (Hong Kong Police Force, 2007-2011). According to the Hong Kong Correctional Services Department (2012), as of December 2011, a total of 9,190 adult men, and 831 young men aged 21 years and under, were imprisoned. In addition to nonviolent offenders, these numbers include those who were incarcerated for a violent offense.

Within the last decade, empirical studies that sampled Hong Kong incarcerated offenders have been scarce, let alone those that focus only on offenders who were imprisoned for a nonviolent offense. Most published work focused on the general operational functions of and services provided by the correctional services (e.g., Jones & Vagg, 2007; Laidler, 2009; Lo, 2008; Lo, Wong, Chui, Zhong, & Senior, 2010; Tam & Heng, 2008), assistance provided to the inmates by volunteers (e.g., Chui & Cheng, 2013a, 2013b), and ex-inmates’ accounts of their imprisonment experience (e.g., Adorjan & Chui, 2012; Chui, 2005). No study attempted to examine the reoffending rate or risk factors of offenders incarcerated for a nonviolent offense. This study attempts to investigate the recidivism rate and risk factors of this often overlooked incarcerated offender group. Prior to the discussion of this study, an overview of the Hong Kong correctional services and factors associated with offender recidivism are presented.

An Overview of the Hong Kong Correctional Services Department

Since 1997, Hong Kong has been a special administrative region (SAR) under the ruling of the People’s Republic of China. It has been regarded as having one of the lowest crime rates among developed cities or territorial regions. Contrary to criminologists’ predictions that crime rates would be likely to soar after its handover to the Chinese sovereignty by the British government in 1997 (Traver, 2009), Hong Kong has evidenced a relatively low crime rate (1,081 per 100,000 in 2010) when compared with other developed cities such as Tokyo (1,640 per 100,000) or New York (2,257 per 100,000; Hong Kong Police Force, 2012). In view of such an arguably low crime trend, the effort put in by the correctional system cannot be understated. In Hong Kong, the Correctional Services Department (CSD) operates 24 correctional institutions scattered throughout Hong Kong Island, Kowloon, the New Territories, and outlying islands.

In addition to the discipline aspect of the correctional institutions, rehabilitation is also emphasized (Laidler, 2009). Similar to the Hong Kong probation system (Chan & Chui, 2012; Chui & Chan, 2011a, 2011b, 2012a), the CSD consistently maintained “the rehabilitative ideal of imprisonment even when it became discredited in the West” (Jones & Vagg, 2007, p. 597). The motto “we care” adopted by the CSD has further underscored its commitment to instill rehabilitative components in offender custody (Laidler, 2009). Hence, five units under its Rehabilitation Division have been established to offer rehabilitative programs for the inmates: (a) Rehabilitation Unit (Assessment Services), (b) Education Unit, (c) Industries and Vocational Training Section, (d) Psychological Services Section, and (e) Rehabilitation Unit (Welfare and Counseling Services). As part of their training for eventual community reintegration on release, inmates are assigned to an assortment of daily tasks, which include, among others, manufacturing and laundry work that are fashioned to the current labor market (Laidler, 2009).

Static and Dynamic Risk Factors of Recidivism

An important objective of the correctional function of criminal justice systems is to reduce the risk of offender recidivism. For this reason, risk factors for reoffending should be identified and attempts made to intervene with them. For an effective intervention strategy, it is argued that it should follow the human service principles of risk–need–responsivity (RNR) and professional discretion (Andrews & Bonta, 2003; Andrews, Bonta, & Wormith, 2006). There are two primary types of risk factors: static and dynamic factors. Simply put, static factors are characteristics that are immutable, whereas dynamic factors are attributes that are responsive to change (Bonta, 1996) through rehabilitative and therapeutic interventions (Resnick, Ireland, & Borowski, 2004; van der Put et al., 2012). These factors can be further subdivided into individual- and environmental-level risk factors.

Static risk factors at the individual level include gender, intelligence, neuropsychological attributes (Vermeiren, de Clippele, Schwab-Stone, Ruchkin, & Deboutte, 2002; Vermeiren, Schwab-Stone, Ruchkin, de Clippele, & Deboutte, 2002), type of offense committed, number of prior convictions (Archwamety & Katsiyannis, 1998; Chui & Chan, 2012a; Jung & Rawana, 1999; Katsiyannis & Archwamety, 1997; Lattimore, Visher, & Linster, 1995), onset age of delinquent behavior, age of first adjudication, and intensity of criminal careers (Ang & Huan, 2008; Cottle, Lee, & Heilbrun, 2001; Dean, Brame, & Piquero, 1996; Loeber & Farrington, 1998; Loeber, Farrington, Stouthamer-Loeber, & Raskin White, 2008; Vermeiren, de Clippele, & Deboutte, 2000). Static environmental risk factors, conversely, include parental abuse and neglect, and conflicts with parents (Benda & Tollet, 1999; Piquero, Brame, & Moffitt, 2005).

In contrast, dynamic individual risk factors include personality dispositions (e.g., high impulsivity, high negative emotionality, weaker social bond, and high pro-offending attitudes; Caspi, Moffitt, Stouthamer-Loeber, Krueger, & Schmutte, 1994; Ge, Donnellan, & Wenk, 2003; Krueger et al., 1994; Nagin & Tremblay, 1999; Willis & Grace, 2009), psychopathological attributes (e.g., conduct and antisocial personality disorders, and psychopathic traits; Das, 2008; Katsiyannis, Zhang, Barrett, & Flaska, 2004; Kotler & McMahon, 2005; van Dam, Janssens, & De Bruyn, 2004; Vermeiren, Jespers, & Moffitt, 2006; Walters, 2003), and substance use (Abnernathy, Massad, & Romano-Dwyer, 1995; Ford, 2005). Dynamic environmental risk factors includes parental criminality (Hagell & Newburn, 1996), criminal peers, residing in a disadvantaged neighborhood, and influences from a poor economic and social environment (Kubrin & Stewart, 2006; Mennis & Harris, 2011; Oberwittler, 2004; Pardini, Obradovic, & Loeber, 2006; Rankin & Quane, 2000).

Research Questions

This study aims to explore the common recidivism risk factors for offenders who were previously incarcerated (i.e., repeat offenders). The study of recidivism of convicted offenders has been lacking in Hong Kong. To the authors’ knowledge, only one recidivism study has been published with a Hong Kong sample within the last decade. In Chui and Chan’s (2012a) study, a group of 92 male juvenile probationers (aged 14 to 20 years) were examined for their recidivism rate within a 6-month follow-up period. A 30% recidivism rate was recorded within the 6-month period, with 82% and 18% readjudicated for a nonviolent and violent offense, respectively. Several reoffending predictors were identified: negative affect, self-perceived life problems, and self-esteem. Besides this study, no other research effort on this topic has been conducted in Hong Kong. Hence, the general purpose of this study is to extend the empirical efforts in this area by exploring the recidivism rate and risk factors of incarcerated offenders who were incarcerated for a nonviolent offense. In this study, the term “offender recidivism” is referred as “offender reincarceration.” The series of questions of particular importance to this study were as follows:

Research Question 1: How many of these incarcerated offenders were recidivists?

Research Question 2: What is their reoffending rate?

Research Question 3: What are their common recidivism risk factors?

Time to Nonviolent Recidivism

Past studies have found that most recidivist offenders typically reoffend within the 1st year after their release, but the recidivism rate continues to increase in the first 5 to 8 years on release (Mulder, Brand, Bullens, & van Marle, 2011). Of note, many studies have indicated higher reoffending rates with nonviolent offenders over a 3-year period. More interestingly, a number of studies have reported higher rates of recidivism among nonviolent offenders than other groups of offenders (Bureau of Justice Statistics, 2002; McCoy & Miller, 2013; Washington State Sentencing Guidelines Commission, 2005). For instance, the Bureau of Justice Statistics (2002) found an 80% recidivism rate for property offenders over a 3-year assessment period. McCoy and Miller (2013), however, reported that 52% of male and 48% of female nonviolent offenders were rearrested an average of 2 years after release, with the most common recidivistic offense being drug offenses.

Method

Participants and Procedure

From the 24 Hong Kong correctional institutions that include drug addiction treatment centers, rehabilitation centers, training centers, detention centers, and prisons, 12 institutions were selected randomly, using a computerized randomization process. The selected institutions were located in different geographical regions: Hong Kong Island, Kowloon, New Territories, and the outlying islands. The participants in this study were 278 incarcerated male offenders convicted of a nonviolent offense. To explore the reincarceration dynamics of the imprisoned offenders, all participants sampled were recidivists who were previously imprisoned. Specifically, participants recruited were those who had been readmitted to the CSD institutions within 5 years of their prior release. These participants were surveyed once and were asked to recall information over a 3-year period.

Following approval from both the CSD and university’s institutional review board (IRB), the participants’ informed consent was acquired with no financial rewards or benefit in other forms (e.g., reduction in incarceration term, additional daily recreational time). For those under the age of 18 years, in addition to their personal consent, parental consent was also sought. Importantly, their participation in this study was completely voluntary. An average response rate of 75% was obtained. The recruitment process was assisted by the personnel of the CSD who sought the participants’ initial willingness to participate in this study. Anonymous paper/pencil questionnaires were administered to consenting participants. Participants were assured that their responses would be kept confidential and used only for this study. Questionnaires were administered to a group of 4 to 10 participants at a time by two trained research assistants (RAs). Both RAs had an academic background in behavioral sciences and were trained to administer the questionnaire in a consistent manner. Discussion among participants regarding the questionnaire content was prohibited during the survey administration. An average of 20 min was taken by the participants to complete the questionnaire.

Demographic Characteristics of the Sample

Among all participants, who aged between 17 and 75 years (M = 34.81, SD = 12.07), nearly 59% were young adults between 21 and 40 years (refer to Table 1). With regard to the type of nonviolent offense committed, nearly two thirds of them were convicted of a nonproperty offense (e.g., drug-related and financial crimes). About 60% of the participants were unmarried at the time of the data collection, and about 74% of them were at least secondary-school-educated. Roughly 41% of them were found to have at least six previous convictions, with an average of 6.85 convictions (SD = 6.15). Their average age before release was 32.80 years (SD = 12.29), with most (53%) aged between 21 and 40 years. Interestingly, more than two thirds (72%) of the participants claimed to be an active triad member.

Table 1.

Sample Demographic Characteristics (N = 278).

Variable	N	%
Age group (N = 275)
17 to 20 years	35	12.7
21 to 40 years	161	58.5
41 to 60 years	72	26.2
61 years and above	7	2.6
Type of crime committed (N = 278)
Nonviolent property crime	98	35.3
Nonviolent nonproperty crime	180	64.7
Marital status (N = 274)
Single	163	59.5
Married	76	27.7
Separated/divorced	31	11.3
Widowed	4	1.5
Highest educational level (N = 275)
Primary school or below	73	26.5
Secondary school	187	68.0
Vocational training	11	4.0
University or above	4	1.5
Number of prior convictions (N = 272)
1 to 5 convictions	160	58.8
6 to 10 convictions	65	23.9
11 to 15 convictions	26	9.6
16 convictions and above	21	7.7
Age of prior release (N = 278)
20 years and below	57	20.5
21 to 40 years	148	53.2
41 to 60 years	66	23.7
61 years and above	7	2.5
Self-proclaimed triad member (N = 264)
Yes	190	72.0
No	74	28.0

Measures

For the purpose of this study, criminal recidivism was operationally defined as reoffending that eventually resulted in conviction and incarceration. Official recidivism by a different time period of interest served as the binary outcome variable (i.e., 0 = not reincarcerated, 1 = reincarcerated). Aside from examining the reincarceration rate of imprisoned nonviolent offenders in Hong Kong,¹ a number of static (e.g., offending history) and dynamic factors (e.g., psychological attributes, interpersonal attachment and relationship, and situational criminogenic influences) were explored to uncover potential risk factors of recidivism for offenders who commit nonviolent offenses. A number of scales were constructed to assess the participants’ psychological attributes (i.e., pro-offending attitudes, impulsivity, and negative self-perception), attachment or relationship with others (i.e., familial detachment, deviant peer attachment, and general prosocial attachment deficiency), and potential exposure to criminogenic circumstances (i.e., domestic and community criminogenic exposures), in addition to questions asking for the participants’ demographic information and offending history. Items of these scales were measured on a 4-point Likert response format (1 = strongly disagree; 4 = strongly agree).² Of note, no significant mean differences were found in these measures between participants who were convicted of a property and nonproperty offense.

Pro-offending attitudes (Cronbach’s α = .78)

In this study, nine items were used to assess the participants’ general attitudes toward offending. The overall score was the sum of all nine items (ranging from 9 to 36), with a higher pro-offending attitudes score denoting a higher favorable attitude toward offending. Sample items include, “Crime allows me to get things that I want” and “Committing crime is exciting and rewarding.” Participants in this study scored an average 19.38 for this scale (SD = 4.13).

Impulsivity (Cronbach’s α = .51)

The participants’ impulsivity was measured on two items: “I cannot control my impulses” and “I cannot resist temptation.” The total score ranged from 2 to 8 points, with a higher score indicating greater impulsivity. The alpha coefficient of this measure was below the acceptable level of .70 (see Cronbach, 1951). However, the alpha estimate should be interpreted cautiously given that the Cronbach’s α mainly measures the “interrelatedness of the items” (Sijtsma, 2009). Besides, a low internal consistency estimate could partly be due to the highly skewed distributions of included items as this reduces “the size of the correlation between items, and therefore, also the alpha” (Straus & Kantor, 2005, p. 25). Importantly, Cronbach’s α is “dependent not only on the magnitude of the correlations among items, but also on the number of items” (Streiner & Norman, 1989, p. 64). The participants scored an average point of 4.93 (SD = 1.31) in impulsivity.

Negative self-perception (Cronbach’s α = .88)

Nine items were used to assess the participants’ negative perceptions of themselves. The overall score of this measure was determined by summing the scores of all items, which ranged from 9 to 36 points. A higher score denotes higher negative observation or perception of themselves. Sample items include, “I am a victim of this unjust world” and “Society will not accept offenders like me.” The participants scored an average 20.68 points (SD = 5.31).

Familial detachment (Cronbach’s α =.79)

A measure that consists of four items was used to assess the participants’ lack of positive (or poor) attachment and relationship with their family members. The overall score of this measure ranged from 4 to 16 points, with a higher score indicating poor attachment and relationship with their family members. Sample items include, “I often have conflicts with my family” and “My family did not and will not support me.” An average point of 8.79 (SD = 2.56) was found among the participants.

Deviant peer attachment (Cronbach’s α = .69)

The participants’ attachment with delinquent and deviant peers was measured with four items in this study. The sum of all these items ranged from 4 to 16 points. A higher point in this measure denotes greater attachment with and influence from delinquent and deviant peers. Sample items include, “Most of my friends are criminals” and “Most of my friends are associated with triad societies.” The participants scored an average point of 9.31 (SD = 2.58).

General prosocial attachment deficiency (Cronbach’s α = .76)

To assess the participants’ overall lack of prosocial attachment with others, three items were used. The sum of all items ranged from 3 to 12 points, with a higher score indicating greater absence of prosocial attachment with other individuals. Sample items include, “There is no individual whom I can trust” and “I do not have anyone who can share my problems.” On average, the participants scored 6.53 points (SD = 2.11).

Domestic criminogenic exposure (Cronbach’s α = .74)

Two items were used to assess the participants’ exposure to criminal behavior within their family setting: “I am aware of the criminal activities of my family members” and “I am aware of the drug-taking habits of my family members.” The total score of this measure ranged from 2 to 8 points, with a higher score indicating greater criminality exposure and influence from their family members. The participants scored an average 2.80 points (SD = 1.22).

Community criminogenic exposure (Cronbach’s α = .85)

To assess the criminality influence from the participants’ community, three items were used: “I am living in a community full of triad members,” “I am aware of the many opportunities to commit crime in the community where I am currently staying,” and “Crimes regularly occur in my community.” The sum of scores ranged from 3 to 12 points, with a higher score denoting greater criminality exposure within the participants’ residential community. On average, the participants scored 7.22 points (SD = 2.07).

Analytic Strategy

In this study, several statistical analyses were performed. Pearson correlations were used to examine the interrelationships of different constructs. Logistic regression was performed using the sample of 278 incarcerated male offenders convicted of a nonviolent offense to identify the predictors of recidivism risk within the 1st, 2nd, and 3rd years following release. The offenders’ incarceration on reconviction was their recidivism indicator. Participants were included in different tested time-point models according to their respective recidivism time-point. To illustrate, 58 participants were included in the first time-point model (i.e., reincarcerated within 1 year post-release), 188 participants in the second time-point model (i.e., reincarcerated within 2 years post-release), and 243 participants in the final time-point model (i.e., reincarcerated within 3 years post-release). Of note, despite the number of previous imprisonment, the offenders’ immediate past conviction that led to incarceration was used as the benchmark for the analyses of their recidivism rates and risk factors. Static (i.e., number of prior convictions and age of prior release) and dynamic (i.e., pro-offending attitudes, impulsivity, negative self-perception, familial detachment, deviant peer attachment, general prosocial attachment deficiency, domestic criminogenic exposure, and community criminogenic exposure) factors as independent variables were entered in three multivariate analytic models. Pearson correlations of the tested constructs were computed and findings did not reveal any correlations at or above 70, indicating no collinearity.

Results

Offending History, Psychological Characteristics, Interpersonal Relationships, and Criminogenic Exposures

Table 2 presents the interscale relationship of different constructs of interest: two offending history constructs (i.e., number of prior convictions and age of prior release), three psychological characteristics (i.e., pro-offending attitudes, impulsivity, and negative self-perception), three interpersonal attachment constructs (i.e., familial detachment, deviant peer attachment, and general prosocial attachment deficiency), and two criminogenic exposure constructs (i.e., domestic criminogenic exposure and community criminogenic exposure). Significantly, the offenders’ number of prior convictions and age at time of prior release were positively correlated with their negative self-perception (r = .24 and r = 28, p < .01), familial detachment (r = .21 and r = .19, p < .01), and general prosocial attachment deficiency (r = .24 and r = .25, p < .01), respectively. Interestingly, all psychological characteristics, interpersonal attachment, and criminogenic exposure constructs were significantly and positively intercorrelated, with the strength of the correlational effects ranging from .24 to .67 (p < .01).

Table 2.

Pearson Correlations of the Observed Indicators of Convicted Offenders of Nonviolent Property and Nonproperty Crime (N = 278).

Indicators	NC	AR	PO	IP	NS	FD	DP	AD	DC	CC
Number of prior convictions (NC)	1.00
Age of prior release (AR)	0.49**	1.00
Pro-offending attitudes (PO)	0.12	0.07	1.00
Impulsivity (IP)	0.12	0.03	0.53**	1.00
Negative self-perception (NS)	0.24**	0.28**	0.58**	0.41**	1.00
Familial detachment (FD)	0.21**	0.19**	0.43**	0.38**	0.59**	1.00
Deviant peer attachment (DP)	0.07	−0.05	0.57**	0.50**	0.56**	0.47**	1.00
Attachment deficiency (AD)	0.24**	0.25**	0.43**	0.24**	0.55**	0.67**	0.38**	1.00
Domestic criminogenic (DC)	0.11	0.08	0.41**	0.23**	0.28**	0.31**	0.42**	0.27**	1.00
Community criminogenic (CC)	0.07	0.02	0.52**	0.45**	0.50**	0.37**	0.59**	0.25**	0.32**	1.00

p < .01.

Offending History and Reincarceration Rates

The offenders’ number of prior convictions and age at prior release, and their 1-, 2-, and 3-year reconviction rates were examined for their interindicator relationships (refer to Table 3). The offender’s age at prior release was positively related with their number of prior convictions (r = .49, p < .01). Surprisingly, only the offenders’ number of prior convictions was positively correlated with their reconviction rate within the 1st (r = .06, p < .05), 2nd (r = .16, p < .01), and 3rd year (r = .09, p < .05) on release.

Table 3.

Pearson Correlations of the Observed Indicators (N = 278).

Indicators	NC	AR	1YR	2YR	3YR
Number of prior convictions (NC)	1.00
Age of prior release (AR)	0.49**	1.00
1-year reconviction rate (1YR)	0.06*	−0.02	1.00
2-year reconviction rate (2YR)	0.16**	0.10	0.36**	1.00
3-year reconviction rate (3YR)	0.09*	0.01	0.20**	0.55**	1.00

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

Identifying the Reincarceration Risk Factors of Nonviolent Offenders

Table 4 presents the findings of three logistic regression models with the odds ratios (ORs), whereby reincarceration, at different time periods, is the predicted outcome. In this study, adjusted ORs were computed, exp (B) −1 × 100 = adjusted OR, to report the statistically significant effects on the percentage change in the odds. In terms of the offenders’ criminal background, their number of prior convictions was found to be the only significant predictor of their reoffending risk. To illustrate, when a unit increased in the number of prior convictions, the offenders’ odds of reincarceration was increased by 6% within 2 years and 10% within 3 years on their release. The offenders’ negative self-perception and impulsivity were found to be significant psychological attribute predictors of their reincarceration risk. Unexpectedly, every one-unit increase in the offenders’ negative self-perception, resulted in the odds of reoffending that led to reincarceration decreasing by 9% within the 1st year, and 14% within the 2nd and 3rd year on release. In contrast, the odds of the offenders being reincarcerated within 3 years following release was increased by 47%, if they were impulsive.

Table 4.

Logistic Regression of Convicted Offenders’ 1-Year, 2-Year, and 3-Year Official Recidivism Rates (N = 278).

	Model I (1-year recidivism)				Model II (2-year recidivism)				Model III (3-year recidivism)
Predictor variables	B	SE	OR	LCI, UCI	B	SE	OR	LCI, UCI	B	SE	OR	LCI, UCI
Number of prior convictions	0.03	0.03	1.03	0.97, 1.09	0.06	0.03	1.06*	1.00, 1.13	0.09	0.05	1.10*	0.99, 1.21
Age of prior release	−0.01	0.02	0.99	0.96, 1.02	0.02	0.01	1.02	0.99, 1.05	−0.01	0.02	1.00	0.96, 1.04
Pro-offending attitudes	−0.07	0.06	0.93	0.83, 1.04	−0.04	0.05	0.97	0.88, 1.06	−0.07	0.07	0.94	0.82, 1.07
Impulsivity	0.20	0.15	1.22	0.90, 1.65	0.15	0.13	1.16	0.89, 1.51	0.39	0.20	1.47*	1.00, 2.16
Negative self-perception	−0.09	0.05	0.91*	0.83, 1.01	−0.15	0.04	0.86***	0.79, 0.94	−0.15	0.06	0.86*	0.76, 0.97
Familial detachment	0.18	0.10	1.20*	1.00, 1.45	0.06	0.08	1.06	0.90, 1.25	0.01	0.11	1.01	0.81, 1.26
Deviant peer attachment	0.05	0.09	1.06	0.87, 1.26	0.19	0.08	1.21*	1.03, 1.42	−0.02	0.12	0.98	0.77, 1.24
Prosocial attachment deficiency	0.17	0.11	1.19	0.96, 1.47	0.18	0.10	1.20*	0.98, 1.46	0.23	0.15	1.26	0.94, 1.70
Domestic criminogenic exposure	0.24	0.14	1.27	0.96, 1.69	−0.09	0.13	0.91	0.70, 1.18	−0.51	0.18	0.60**	0.43, 0.86
Community criminogenic exposure	−0.10	0.11	0.91	0.74, 1.12	0.12	0.09	1.12	0.94, 1.34	0.31	0.13	1.34*	1.05, 1.75
−2 log likelihood	258.58				308.95				180.24
Model chi-square	20.69*				32.01 ***				24.72**
Hosmer–Lemeshow test	χ² (8) = 9.81, p = .28				χ² (8) = 4.46, p = .81				χ² (8) = 9.52, p = .30
Nagelkerke R²	0.11				0.11				0.16
AUC	0.71				0.72				0.69

Note. OR = Odds ratio; LCI = lower confidence interval; UCI = upper confidence interval; AUC = area under the curve.

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

As regards the offenders’ relationship with others in predicting their recidivism risk, when the offenders were lacking positive attachment with their families, the odds of their being reincarcerated within the 1st year following release increased by 20%. Besides, the odds of the offenders being reincarcerated within 2 years after release were found to increase by 21% and 20%, when they were associated with deviant peers and absence of prosocial attachment with others, respectively. Pertaining to the situational recidivism risk factors, when the offenders were exposed to criminogenic incidents within their community, the odds of reoffending and eventually being reincarcerated increased by 34% within the 3rd year on release. However, unexpectedly, every one-unit increase in the offenders’ exposure to criminogenic incidents within their family resulted in the odds of their reincarceration decreasing by 40% within 3 years on release.

Overall, chi-square analyses of these three testing models indicated significant models fit (χ² = 20.69, p < .05 for Model I, χ² = 32.01, p < .001 for Model II, and χ² = 24.72, p < .01 for Model III). The Hosmer–Lemeshow Test (Hosmer & Lemeshow, 2000) of all models also suggested no difficulties with the fit model. The area under the curve (AUC) of the receiver operating characteristics (ROC) yielded values of 0.71 for Model I, 0.72 for Model II, and 0.69 for Model III, suggesting that all these models reached an adequate level of predictability, specificity, and sensitivity (see Kleinbaum & Klein, 2010).

Discussion

This study is important not only in contributing to the repertoire of knowledge from an empirical perspective, but also informing the field practitioners of the findings to facilitate the development of correctional criminology in Hong Kong. Perhaps, these findings may be of practical utility in other countries as well. Particularly, this study of recidivism was substantial for at least two reasons. First, identification of key risk factors specific to the reincarceration of nonviolent offenders is critical for intervention planning to prevent future criminal behavior (Harland, 1996), whereby evidence indicates that programs and services targeting specific reoffending risk factors are five times more effective than those without an adequate conceptual model (Izzo & Ross, 1990). Second, according to Duncan, Kennedy, and Patrick (1995), identification of reoffending risk factors could help determine the suitability of the placement in different intervention programs based on the differential criminogenic needs of the offenders. Evidence suggests that dynamic risk factors are likely to reduce as age increases, which denotes the importance of early intervention (van der Put et al., 2011).

Overall, the findings indicate that the effect of dynamic risk factors appeared to be more dominant in determining the propensity to reoffend that led to the reincarceration among this group of nonviolent offenders. Interestingly, a display of positive self-perception was considered to be a significant predictor of recidivism across three tested time points. Unexpectedly, this result was inconsistent with many past studies. Positive self-perception or evaluation in this context could be referred to as having a high self-esteem. Existing literature indicates that low self-esteem predicts offending persistency (e.g., Benda, 2001; Caspi et al., 1994; Krueger et al., 1994). Perhaps, the finding of this study could be explicated from a cognitive model of offending behavior. Baumeister (1997) reasoned that offending behavior is likely to result from an inflated sense of self-esteem or grandiosity as part of the “macho” cover-up of the individual’s embarrassment or low self-esteem (see also Walker & Bright, 2009). This phenomenon has been evidenced more in relation to violent behavior (e.g., Beck, 1999; Gillespie, 2005; Salmivalli, 2001). Furthermore, an acceptance of offending behavior, particularly with violence, was also found to be associated with a false consensus that validates offending behavior as a response to the strength of insult or humiliation imposed by others, which possibly falsely inflates the level of self-esteem (Walker, 2005; Walker & Gudjonsson, 2006).

In addition, substantial differences in risk factors were observed in the participants’ reoffending predictors based on different time periods. In line with the extant literature of the importance of positive family relationship in reducing the recidivism risk (e.g., Benda & Tollet, 1999; Watt, Howells, & Delfabbro, 2004), participants who were having a poor attachment and social bonding with their family were in higher likelihood to reoffend and be reincarcerated within a 12-month period on release when they need close support to help them reintegrate into the community. Those who reincarcerated for an offense committed up to 2 years subsequent to release, however, were found to have a weaker prosocial attachment pattern as indicated by close association with deviant peers and a lack of prosocial relationship with others. These predictive factors for reoffending were also previously reported (e.g., Dishion, McCord, & Poulin, 1999; Mennis & Harris, 2011). These findings are substantially in line with Hirschi’s (1969) social control theory, which essentially maintains that delinquent and criminal behavior occurs because of weak social bonds with conventional society and individuals. Moreover, continuing differential association or social interaction with deviant peers was also asserted to be an important predictor of persistence in offending in Akers’ (1998) social learning theory.

However, in the current study, relatively different recidivism predictor factors were observed for offenders who were reincarcerated within a 3-year period. In addition to impulsivity as a significant predictor of persistent offending—as was theorized in Gottfredson and Hirschi’s (1990) self-control theory and consistently found in other studies (e.g., Miller-Johnson, Coie, MAumary-Gremaud, Lochman, & Terry, 1999; Nagin & Tremblay, 1999; see Lee & Hinshaw, 2004, for an exception)—environmental influences seemed to be more dominant as indicated by both domestic and community exposures to criminality. These findings were consistent with the literature, whereby the familial (e.g., Hagell & Newburn, 1996; Huan, Ang, & Lim, 2010; Katsiyannis et al., 2004) and neighborhood (e.g., Kubrin & Stewart, 2006; Mennis et al., 2011) effects were reported to at least partly predict a reoffending propensity. It is notable that the number of prior convictions was the only significant static recidivism risk factor for the 2- and 3-year risk of reoffending. This predictor was also previously reported (e.g., Andrew & Bonta, 2003; Bensda, Corwyn, & Toombs, 2001; Hoge, 1999; Jung & Rawana, 1999; Lattimore et al., 1995).

Looking at the reincarceration rates at three different time periods, an interesting trend emerged. Within a 12-month postrelease period, 21% of the nonviolent offenders were reconvicted and reincarcerated. Compared with other studies (e.g., a 30% 6-month probation violation rate in 92 male juvenile probationers in a study by Chui & Chan, 2012a; a 31% 1-year violent failure rate found in 618 mentally disordered male inmates by Harris, Rice, & Quinsey, 1993; a 26% recidivism rate in 328 juvenile probationers by Onifade et al., 2008; and a 46% 8-month failure rate found in 104 juvenile offenders by Vermeiren et al., 2000), this 1-year reoffending rate was arguably low. Perhaps it could be reasoned that it is associated with the nonviolent nature of the initial index offense. This reincarceration rate increased to 68% within 2 years postrelease. Interestingly, this reoffending rate was relatively consistent with the rate found in other comparable studies (e.g., 65.2% rate of recidivism found in 414 serious violent juvenile offenders by Benda et al., 2001; a 70.1% failure rate that excluded misdemeanors and vandalism found in 728 serious juvenile offenders by Mulder et al., 2011). Within the 3-year postrelease period, 87% of the ex-convicts in this study were reincarcerated. This rate was fairly high compared with other 3-year follow-up studies (e.g., a 38.5% failure rate found in 480 male boot campers by Benda, 2001; a 15% rate of recidivisim and parole violations found in 299 incarcerated juvenile offenders by Katsiyannis et al., 2004; a 67.5% reoffending rate found in 272,111 former inmates in 15 states in the United States, with 73.8% of property offenders by Langan & Levin, 2002). However, readers should be mindful of different operational definitions of “recidivism” used in these studies.

Implications of the Findings

Nearly all the ex-inmates examined in this study were reincarcerated within 3 years subsequent to their release. This high reincarceration rate was documented despite the nonviolent nature of the offenders’ initial index offense, although violent offenders have been found to have a higher risk of recidivism (e.g., Loeber & Farrington, 2000). Thus, serious concerns should be raised about the operational purposes and the effectiveness of the Hong Kong correctional services. The current rehabilitative strategies ought to be strengthened. Ideally, more refined and evidence-based intervention programs are required tailored to the criminogenic needs of different offenders.

More efforts need to be attempted to efficiently target incarcerated offenders’ reoffending risk factors. The offenders’ dynamic criminogenic needs should be prioritized. As evidenced, a number of domains such as the offenders’ psychological conditions, interpersonal relationships, and environmental influences are critical in affecting their propensity to recidivate. None of these domains should be intervened with separately or individually, as an accumulation of risk factors in multiple domains increases the probability to not just reoffend, but also behave criminally in general (Loeber, Slot, & Stouthamer-Loeber, 2008). Thus, all intervention target areas should be closely monitored concurrently during the process. Although static risk factors are not amenable to designed change, they should also be considered when conceptualizing and planning of intervention efforts.

To illustrate, intensive psychological services, such as positive psychological self-development in the areas of consequential thinking patterns, recognition of and sensitivity to the feelings of others to target the offenders’ inflated self-esteem, and interpersonal cognitive problem-solving interventions and anger management that aim to reduce impulsivity and instantaneous anger or low frustration tolerance, should be tailor-made for each offender, to meet his distinctive criminogenic needs. Social skills training should be stressed to foster prosocial relationships with others, especially with family members. Intervention strategies that focus on healthy family functioning could help to foster positive familial social bonding, which in turn may reduce a reoffending propensity aided by family support to desist from offending (Chan & Chui, 2012, 2013; Chui & Chan, 2011b, 2012a, 2012b, 2013a, 2013b). Moreover, efforts should be made to raise awareness of peer influence, mainly those who involved in delinquency (Benda, 2001).

Limitations and Future Research Directions

Several cautionary methodological caveats should be noted. This study was retrospective and cross-sectional in nature. In future research, a prospective longitudinal design with repeated measures should be considered. By doing so, the persistence of criminal behavior could be extensively studied. In this study, only a small group of incarcerated nonviolent offenders were examined. Importantly, the female population was not sampled in this study. Thus, findings reported here cannot be generalized to all incarcerated offenders convicted of a nonviolent offense. For future studies, it will be meaningful to investigate whether the current findings hold for not just a larger sample of incarcerated nonviolent offenders, but also across genders. In line with this issue, current findings are limited to the incarcerated population. Potential differences may be present for those who were initially arrested but not convicted. Therefore, it will be fruitful to explore the similarities and differences of these two groups of nonviolent offenders. Moreover, this study was limited to the examination of the recidivism profile of nonviolent offenders. The sample of violent offenders collected in this study was insufficient to perform sound statistical comparative analyses with nonviolent offenders. Hence, future research may consider exploring the differences between incarcerated nonviolent and violent offenders for their potential differential reoffending risk factors.

With regard to the predictive effect of the tested offending history, psychological correlates, and environmental influences, the effects of these risk factors were limited by the use of self-reported information. It is noteworthy that offenders have a predisposition to under-report their criminal behavior and to normalize their perceived attitudes toward criminal conduct (Breuk, Clauser, Stams, Slot, & Doreleijers, 2007). However, the use of official data as the benchmark for the recidivism rate also has an inherent risk of underestimating the actual nature of an offenders’ involvement in criminal activities. Their reoffending behaviors are not always detected, which leads to being under-registered in the official systems (van der Put et al., 2011). Thus, future research should consider using both official and self-reported data to obtain findings with a higher predictive power. Moreover, current findings were limited with the use of the regression approach in predicting recidivism outcomes. Other routinely used probabilistic analytic methods such as Cox regression models and survival time to failure models (see Schmidt & Witte, 1988 for a review) could be considered in future research to produce findings with better explanatory power. Schmidt and Witte (1989) even suggested the application of split-population survival time models in studying recidivism. This method is capable of avoiding the overprediction of a long-term failure rate, which is a major limitation in most traditional failure time models.

In summary, these limitations notwithstanding, the current findings have contributed to an under-researched component of the Hong Kong correctional system. Findings of this study indicate that the offenders’ reoffending propensity accounts for a multitude of risk factors composed of their offending history, psychological attributes, interpersonal relationships, and environmental influences. Importantly, implications for correctional practice from an intervention perspective and in penal policy making and refinement targeting nonviolent offenders can be drawn from this study. Current findings also highlighted the importance of postrelease services provided by social service workers for this group of offenders to prevent potential relapses. The motto of the Hong Kong CSD is to “support rehabilitative offenders for a more inclusive society.” Undoubtedly, released inmates potentially face a number of pressing challenges on release, including locating accommodation, securing employment, receiving follow-up treatment, and for some, complying with the supervision terms (Kubrin & Stewart, 2006). Hence, resources, services, and amenities from both the correctional postrelease services and from the community are needed for ex-inmates to successfully reintegrate into conventional society, and ultimately to desist from criminal conduct.

Footnotes

Authors’ Note

The views expressed are those of the authors and are not necessarily those of the Correctional Services Department of Hong Kong.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was supported by a grant (SS/5460: Research Study on Performance Measurements of Correctional Services Department’s Programs and Services) from the Correctional Services Department of Hong Kong.

Notes

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