Timing of Employment and Desistance

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CHANGES IN CRIMINAL OFFENDING AROUND THETIME OF JOB ENTRY: A STUDY OF EMPLOYMENTAND DESISTANCE∗

TORBJØRN SKARDHAMAR1 and JUKKA SAVOLAINEN2

1Research Department, Statistics Norway2School of Criminology, University of Nebraska at Omaha

KEYWORDS: desistance, employment, Norway, life-course criminology

Does employment promote desistance from crime? Most perspectives assume that

individuals who become employed are less likely to offend than those who do not.

The critical issue has to do with the timing of employment transitions in the criminal trajectory. The turning point hypothesis expects reductions in offending after job en-

tries, whereas the maturation perspective assumes desistance to have occurred ahead

of successful transitions to legitimate work. Focusing on a sample of recidivist males

who became employed during 2001–2006 (N = 783), smoothing spline regression tech-

niques were used to model changes in criminal offending around the point of entry to

stable employment. Consistent with the maturation perspective, the results showed that

most offenders had desisted prior to the employment transition and that becoming em-

ployed was not associated with further reductions in criminal behavior. Consistent with

the turning point hypothesis, we identified a subset of offenders who became employed

during an active phase of the criminal career and experienced substantial reductions

in criminal offending thereafter. However, this trajectory describes less than 2 percent

of the sample. The patterns observed in this research suggest that transition to employ-

ment is best viewed as a consequence rather than as a cause of criminal desistance.

The relationship between employment and offending is an enduring topic of crimino-logical inquiry (Bushway, 2011; Fagan and Freeman, 1999; Uggen and Wakefield, 2008).The focus of the current study is on the role of employment in criminal desistance,which is defined as the process whereby active offenders reduce and eventually terminate

their criminal careers. Echoing the sociological distinction between structure and agency(Bottoms et al., 2004), the desistance literature offers two accounts of the causal influenceof employment: the turning point and the hook-for-change hypotheses. In addition, the

∗ Additional supporting information can be found in the listing for this article in theWiley Online Library at http://onlinelibrary.wiley.com/doi/10.1111/crim.2011.52.issue-2/issuetoc.We thank Torbjørn Hægeland, David Greenberg, Kjetil Telle, and the three anonymous re-viewers for their critical observations and helpful suggestions. We are particularly grateful toLorine Hughes and D. Wayne Osgood for providing detailed comments of crucial value on mul-

tiple drafts of the article. This research has received financial support from the Norwegian Re-search Council (grant 202453). Direct correspondence to Jukka Savolainen, School of Crimi-nology, University of Nebraska at Omaha, 321 Nebraska Hall, Lincoln, NE 68588-0561 (e-mail:

[email protected]).

C 2014 American Society of Criminology doi: 10.1111/1745-9125.12037

CRIMINOLOGY Volume 52 Number 2 263–291 2014 263



264 SKARDHAMAR & SAVOLAINEN

maturation perspective assumes that employment is best understood as a consequencerather than as a cause of desistance from crime.

The turning point hypothesis treats employment as an exogenous event with the po-tential to set in motion the process of desistance (Laub and Sampson, 2003; Sampson

and Laub, 1993). According to this perspective, desistance happens as an inadvertentresponse to changes in one’s objective life situation. Laub and Sampson (2003: 278–9)used the term desistance by default to describe this outcome: “Many men made a commit-ment to go straight without even realizing it. Before they knew it, they had invested somuch time in a marriage or a job that they did not want to risk losing their investment.”Although Laub and Sampson recognized the role of individual agency, they maintainedthat “most offenders desist in response to structurally induced turning points that serve asthe catalyst for sustaining long term behavioral change” (Laub and Sampson, 2003: 147).

The strong causality suggested by the turning point hypothesis has been qualified byscholars who view subjective change as a precondition for successful exit from a criminal

lifestyle (Giordano, Cernkovich, and Rudolph, 2002; LeBel et al., 2008; Maruna, 2001).For example, Bushway and Reuter (1997) have argued that employment is unlikely tofacilitate desistance in the absence of true commitment to “go straight.” Giordano andcolleagues have advanced a theory of cognitive transformation that—extending the in-vestment analogy—argued that “actors themselves must recognize the need to start savingand develop a high level commitment to the plan” (Giordano, Cernkovich, and Rudolph2002: 1056). According to this perspective, life-course transitions are unlikely to resultin lasting changes in behavior without strong personal desire to undertake a conversioneffort. However, for these intentions to materialize, it may be important to find tangible“hooks for change” in the everyday environment. Thus, under this theory, employment

has the potential to sustain and reinforce the emerging process of desistance.1

Additional strands of criminological theory are skeptical of causal interpretations of either variety (Glueck and Glueck, 1974; Hirschi and Gottfredson, 1983; Morizot andLe Blanc, 2007). These perspectives explain the longitudinal association between steadyemployment and declining criminal activity as a spurious function of maturational reform

(i.e., the age-varying process of “settling down”) (Massoglia and Uggen, 2010). Under thismodel, it would be unrealistic to expect successful employment transitions to occur duringan active phase of the criminal trajectory. Adults who persist in offending face seriousobstacles in the labor market as a direct result of their criminal lifestyle (Pager, 2003).Moreover, as observed by Massoglia and Uggen (2010), active offenders are unlikely to

regard themselves as capable of taking on social roles associated with mature adulthood.The maturation perspective goes one step further than the hook-for-change hypothesis inthat it assumes sustained behavioral transformation, not mere psychological readiness, asa precondition for successful labor market transitions.

Regardless of their positions on causality, each of these three hypotheses implies anegative longitudinal association between employment and crime: The average rate of offending is expected to be higher during the pre-employment period under each per-spective. As described in figure 1, the main point of contention has to do with the timing

of employment in this process. The turning point hypothesis predicts gradual reductions

1. Following LeBel et al. (2008), this process also could be described as a “subjective-social model”of employment and desistance. We find the two perspectives highly compatible in that each treatsthe impact of objective life circumstances as secondary to the subjective intent to change.



TIMING OF EMPLOYMENT AND DESISTANCE 265

Figure 1. Three Ideal-Typical Trajectories Derived from Theory

Time relative to job entry

O f f e n d i n g r a t e

Not employed Employed

Tt−n T0 Tt+n

Turning point

Hook for change

Maturationperspective

in offending after securing employment. The hook-for-change hypothesis expects em-ployment to follow the onset of desistance but suggests that a positive job experience may

reinforce the process. Finally, the maturation perspective expects employment transitionsto follow a period of sustained desistance with no appreciable consequences for the of-fending rate thereafter.

The trajectories presented in figure 1 are ideal-typical representations of complex the-oretical arguments. For example, we do not mean to suggest that the turning point hy-pothesis predicts rigid stability in offending during the pretransition period. The purposeof these contrasts is to highlight key differences in emphasis among the major theoreticalmodels of employment and desistance.

Although there is ample (albeit inconsistent) evidence regarding the association be-tween employment and desistance, as we show in the subsequent discussion, the evidence

on timing is lacking and limited mostly to conflicting accounts from qualitative studies of retrospective narratives. The purpose of the current investigation is to examine the tim-ing issue quantitatively using monthly data on criminal offending and employment fromNorway. Specifically, we address two critical topics that have received limited attention inprior studies of desistance from crime: 1) the timing of employment entries in the criminaltrajectory and 2) the shape of the criminal trajectory following the point of transition toemployment.

PRIOR RESEARCH

The turning point hypothesis was introduced in the monograph Crime in the Making

(Sampson and Laub, 1993) as part of the age-graded theory of informal social control. Theempirical research was based on secondary analysis of males born in Boston around The




Great Depression and followed through 45 years of age (“The Glueck Men”). The overallgoal was to demonstrate the continued salience of social bonds across the adolescent andadult life course. Thus, Sampson and Laub (1993) reported results showing that having astable job at 17–25 years of age reduced the risk of criminal offending at 25–32 years of

age. The effects of adult life circumstances were estimated in the presence of controls forcognitive ability, antisocial traits, prior offending, and socioeconomic risk profile. The factthat job stability predicted desistance over and above these antecedents was interpretedas support for the salience of labor market bonding.

A critical examination of Sampson and Laub’s (1993) job stability index suggests that itmay not be quite as advertised. One item included in this construct is called “work habits,”which indicates the level of reliability and effort in job performance and the degree towhich the subject was considered “an asset to the organization” according to the employer(Sampson and Laub, 1993: 144). A proponent of the maturation perspective might arguethat this is essentially an adult measure of individual differences. As noted by Uggen

(2000: 531): “If employment effects are conditional on good work habits, the putative ‘jobeffects’ are tainted by ‘person effects’ or pre-existing worker characteristics” (see alsoWright and Cullen, 2004).

Subsequent efforts to replicate employment effects with similar methods have pro-duced mixed results. Using contemporary data on juvenile offenders in the United States,Giordano, Cernkovich, and Rudolph (2002) found no association between job stabil-ity and offending rates, leading them to question the degree to which Sampson andLaub’s (1993) conclusions can be generalized beyond their historically specific sample. Incontrast, studies using nationally representative data from contemporary United States(Wright and Cullen, 2004) and Finland (Savolainen, 2009) reported longitudinal evidence

of reduced offending as a function of labor market attachment.Recognition of the limits associated with standard regression techniques has resulted

in the proliferation of studies focused on within-individual changes over the life course(Siennick and Osgood, 2008). The seminal study by Horney, Osgood, and Marshall (1995)used the life-history calendar method to measure monthly changes in the “local life cir-cumstances” in a sample of recidivistic offenders over a period of 3 years. In their re-search, changes in employment status were unrelated to all measures of criminal activitywith one exception: Contrary to expectations, the odds of property crime were higher dur-ing periods of employment. Horney, Osgood, and Marshall noted that their measure of employment covered jobs of any variety, including temporary employment in part-time

work. It is possible that for crime-prone individuals, these settings increase opportunitiesfor theft and other property offenses. Another study examined a sample of male paroleesusing archival data covering a period of 7 years (Piquero, MacDonald, and Parker, 2002).Across most model specifications, full-time employment was unrelated to the risk of ar-rest. As the only exception, employment was found to reduce the risk of arrests for violentcrime among White parolees. Following a similar approach, Griffin and Armstrong (2003)examined a sample of drug-abusing female probationers. The results from this analysisindicated substantial inhibiting effects of employment on drug dealing but not on othercriminal activities.

Ten years after Crime in the Making, Laub and Sampson (2003) published a follow-up study of the Glueck men. The quantitative part of this research estimates the effectsof life-course transitions following a methodology similar to the one used by Horney,Osgood, and Marshall (1995). To allow repeated measurements of life circumstances,




Laub and Sampson (2003) treated marriage and employment as dichotomous time-varying states. This change is notable from their original study in that the new resultsignore variation in the quality of social bonds. The updated analyses are based on datacollected from retrospective life-history calendars of 52 individuals (covering 17–70 years

of age) and a subsample of delinquent boys from the original Glueck data file (n = 419;17–32 years of age). In each type of analysis, the results show systematic and strong evi-dence of reduced offending during periods of employment.

Finally, van der Geest, Bijleveld, and Blokland (2011) examined delinquent offendersreleased from juvenile facilities in the Netherlands and followed them through early adult-hood. The results show evidence of reduced offending during periods of employment.Moreover, the strength of the association was found to depend on employment quality:Individuals hired directly by the employer were more likely to stop offending than thosehired through an agency. According to van der Geest, Bijleveld, and Blokland, this ap-proximates a distinction between regular versus temporary/seasonal employees. Interest-

ingly, job stability—measured as the number of years employed without interruptions—was unrelated to offending.

EVIDENCE FROM LABOR MARKET EXPERIMENTS

By failing to adjust for unobserved heterogeneity in time-varying characteristics, ob-servational studies of intraindividual change usually leave open the possibility that a per-sonal decision to “go straight” precedes change in labor market status. A research designin which randomly selected offenders are provided with employment opportunities is an

effective way to overcome this limitation. Moreover, an experiment that delivers “goodthings” (Laub, Nagin, and Sampson, 1998) through an exogenous process seems particu-larly faithful to the notion of turning points as chance events.

In general, results from employment experiments have been disappointing: “[T]hemost defensible conclusion from experimental evaluations of work programs is that theprograms have a dismal record of jointly improving employment outcomes and of low-ering recidivism” (Bushway and Apel, 2012: 28). Visher, Winterfield, and Coggeshall(2005) conducted a meta-analysis of the effects of community-based employment pro-grams on reoffending. Following the protocol endorsed by the Campbell Collaboration,they identified eight studies with appropriate samples, measures, and a sufficiently rig-

orous experimental design. The mean effect size calculated from this set of studies wasvery small (.03) and statistically not significant, suggesting a lack of evidence showingthat employment programs reduce offending. Indeed, only one study included in themeta-analysis reported a statistically significant negative effect on recidivism (Uggen,2000).

Uggen’s (2000) analysis of data from the National Supported Work DemonstrationProject suggested that employment may serve as a turning point among individuals ma-tured above the peak ages of criminal offending. Although he did not find evidence of an employment effect in the sample at large, in the group 27 years of age or older, therate of recidivism was substantially lower in the treatment versus the control group. Theage-specific nature of this effect seems to be most compatible with the hook-for-changeprocess described in the theory of cognitive transformation (Giordano, Cernkovich, andRudolph, 2002). Older offenders may respond more positively to employment because




they have reached a point at which they are more open to change (Bushway and Reuter,1997; Siennick and Osgood, 2008).

ETHNOGRAPHIC RESEARCH

Laub and Sampson (2003) argued that most men in the Glueck sample desisted as aninadvertent and gradual response to changes in objective life circumstances (“desistanceby default”). The specific evidence in support of this claim comes from life-history nar-ratives of 52 men traced from the original sample of delinquent boys and interviewedaround 70 years of age. These accounts suggest that stable employment is a key charac-teristic distinguishing desisters from those who persisted in crime. As one of the Glueckmen explained: “Being able to go to the store and buy something and not have to steal it. . . that’s important in life . . . what changed my life is work” (Laub and Sampson, 2003:139). Although some men pointed to the stabilizing influence of a “steady paycheck,”others encountered prosocial co-workers or supervisors who acted as effective agents of social control. Shover’s (1996) research on persistent thieves offered independent corrob-oration of the capacity of work to serve as a turning point: “The guy liked me from the jump. And that’s when I hooked up with him. And I went straight a long time without the

intentions of going straight” (Shover, 1996: 127, emphasis in original).Other studies of qualitative life-history data have challenged the turning point hypoth-

esis (Carlsson, 2012; Farrall and Bowling, 1999; Maruna, 2001). Operating from the cog-nitive transformation framework, Giordano, Cernkovich, and Rudolph (2002: 1033) re-ported that both male and female respondents “were very unlikely to build a story of change around the development of a rewarding career, and only a few focus heavily

on stable employment.” Maruna’s (2001: 25) ethnographic study of socioeconomicallymarginalized recidivists in England concluded that it would be unrealistic to expect em-ployment to trigger self-transformation among most offenders.

TIMING IS EVERYTHING

Across each methodological approach, studies of employment and desistance haveyielded mixed results. First, the evidence regarding the longitudinal association betweenemployment and offending is inconsistent. Although several studies have found strongevidence of a negative association, other studies have reported either limited or no asso-ciation, and one study has found a positive association between employment and property

crime (Horney, Osgood, and Marshall, 1995). The evidence regarding the timing of em-ployment in the desistance process is particularly limited. Modes of analysis that comparebetween or within individual differences in offending as a function of employment statushave not been helpful because each theoretical perspective (see figure 1) expects average

offending rates to be lower during periods of employment.Studies using data from employment experiments can establish time order by virtue of

randomized design. However, from the perspective of the hypothesized mechanisms, em-ployment programs targeting offenders may have low external validity. Instead of deliv-ering realistic opportunities for sustained behavioral change, these jobs tend to be shortterm and low quality, and sometimes they may be perceived as borderline punitive bythe client populations (Maruna, 2001; Uggen and Wakefield, 2008). Moreover, becauseindividuals are assigned to jobs under experimental conditions, this method is poorlysuited to study the role of agency and other subjective factors leading up to employment




transition—an issue of major theoretical importance in the desistance literature (LeBelet al., 2008).

At this time, the critical evidence in support of “desistance by default” is limited toqualitative data from life-history narratives (Laub and Sampson, 2003; Shover, 1996).

Although enlightening, such studies likely are more subjective than results based onquantitative analyses of independently validated statistics. For example, Carlsson (2012)has suggested that Laub and Sampson’s findings may be an artifact of demand bias(i.e., the result of asking questions in a manner geared toward eliciting recollections of “turning points”). Moreover, other qualitative studies following the same approach havenot embraced the interpretation proposed by the turning point hypothesis (Giordano,Cernkovich, and Rudolph, 2002; Maruna, 2001).

Our review of the literature suggests a need for quantitative investigations of preem-ployment and postemployment offending trajectories under ordinary (nonexperimental)labor market conditions. Without accurate information about the timing of change in

criminal activity relative to the point of job entry, it is difficult to assess the degree towhich any employment-related decline reflects the employment experience (the causaleffect) versus a preemployment decision to stop offending (the selection effect).

RESEARCH QUESTIONS

Given our interest in the timing of employment events in the criminal trajectory, thefocus of this investigation is on criminally active individuals who end up becoming em-

ployed at some point in their criminal career. Specifically, we address the following twoissues that have received limited attention in prior research:

1. The timing of job entries in the criminal trajectory: Do successful employmenttransitions happen to offenders while they are still criminally active? Or is sus-tained desistance a prerequisite of such events?

2. The shape of the criminal trajectory after the employment transition: Are job en-tries related to subsequent declines in criminal activity as the theories assumingcausal effect predict?

These questions are critical for evaluating the conflicting claims about the role of em-ployment in desistance as summarized in figure 1. The purpose of our research is to ex-amine which theoretical model provides the best fit with the data. Note, however, that it

is not our purpose to establish causality but merely to address the timing issue, which is,of course, a key component of causal inference.

We address these issues with increasing levels of complexity. First, we examine the gen-eral pattern by modeling the average trend in offending before and after the employmenttransition. Next, we attend to the impact of continued employment versus job loss. Priorresearch has suggested that the crime-reducing effect of employment may be limited toperiods of employment, which implies a situational rather than a developmental effect(van der Geest, Bijleveld, and Blokland, 2011). Third, we examine whether these pat-terns vary by age. It has been suggested that older offenders are more likely to embracethe rehabilitative potential of legitimate work (Uggen and Wakefield, 2008).

Independently of chronological age, the consequences of employment for criminal be-havior may depend on the timing of the employment event in the stage of the criminalcareer (van der Geest, Bijleveld, and Blokland, 2011). The hook-for-change hypothesis




suggests that employment is unlikely to promote desistance unless it is preceded by anidentifiable reduction in offending—understood as a signal for readiness to change. Bycontrast, maintaining that sometimes “good things” happen to “bad actors,” the turningpoint hypothesis expects to find a class of offenders who become employed during a rel-

atively active phase of their criminal trajectory. For these individuals, the employmenttransition may serve as a triggering event. Informed by these ideas, the purpose of thefourth and final part of the analysis is to identify subgroups of offenders who make em-ployment transitions at different levels of criminal activity and then to examine variationin the postemployment offending trajectories among these groups.

DATA AND MEASURES

We use data from population registers available in Statistics Norway. This source per-mits the creation of data sets for research purposes through merging individual-level in-

formation from interlinked administrative files, including employment, police, and othercriminal justice records. Although the data are limited to what is available in officialsources, this approach has important advantages over survey-based panel data (Lyngstadand Skardhamar, 2011). First, access to total populations, as opposed to samples, enablesresearch on marginal and hard-to-reach subpopulations without compromising statisticalpower. Second, as these registries cover the entire resident population, problems withmissing data are minimal. Panel attrition is limited to migration and death—events thatcan be easily controlled for in the data. Finally, because these sources track populationevents continuously, the measures are not limited to subjective information provided atdiscrete intervals as is typical in standard panel designs. This property is particularly valu-

able in efforts to study short-term changes in individual outcomes (Siennick and Osgood,2008: 167–8).

EMPLOYMENT SAMPLE

Adhering to the scope conditions of the turning point hypothesis, our investigationis limited to individuals “who reach some reasonable threshold of frequent and serious

offending” (Laub and Sampson, 2003: 22, emphasis added). Thus, the analytic sampleconsists of men, born between 1960 and 1974, with a minimum of five felonies in the policeregistry of solved cases during the period 1992–2000, with the additional requirement that

at least one of these crimes must have occurred later than 1998.2

Furthermore, to capture transitions from criminal lifestyle to legitimate work, we focuson offenders with marginal labor market status (in 1998–2000) prior to entering stable

employment (anytime in 2001–2006), which we define as a job lasting a minimum of 6months.3 Marginal labor market status is operationalized using three criteria: 1) having

2. This additional stipulation is meant to guarantee we include only those who have remained crimi-nally active relatively close to the employment event.

3. The age-graded theory assigns transformative potential to “stable” employment (Sampson andLaub, 1993). Although the meaning of this term remains vague, it is clear that a job lasting a very

short time—say, less than a month—is not expected to serve as a turning point. On the other hand,we find it problematic to define stable employment as a long-term job, e.g., something that lastsfor several years, because it would seem that a sample of that description would include very fewnondesisters by design.




Figure 2. Summary of Sampling Design and Observational Period

UnemployedDecember 2000

1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007

At least five felonies, including one between 1998−2000

Pre− and post−employment offending rate

Unemployed ormarginally employed

Employment entrieslasting 6 months or longer

no employment spells lasting more than 6 months in any of the years 1998–2000, 2) havingno annual income from self-employment over NOK 70,0004 (approximately US$11,500)in any of the years 1998–2000, and 3) being jobless through December 2000 (the monthbefore we started tracking new job entries).

To clarify this design, figure 2 presents a visual summary of the sampling criteria and theobservational period. In simple terms, the resulting sample (N = 783) consists of crime-prone men with unstable employment history who manage to get stable jobs. The purposeof the analysis is to examine monthly changes in the criminal behavior of these individualsbefore and after the employment transition. To ensure our results are robust, the mainanalyses were repeated using a comprehensive set of alternative sampling criteria. Wedescribe these robustness checks at the end of the Results section.

COMPARISON GROUPSome of our analyses include a comparison group of offenders who did not experience

job entries (as defined earlier) but are otherwise similar to the employment group. Giventhat most offenders desist sooner or later (Laub and Sampson, 2003), the purpose of in-cluding a comparison group is to address the possibility that any crime decline observedin the employment sample is an artifact of selecting high-risk individuals at the peak of their criminal careers. In other words, we wish to verify that our results are related to theemployment transition rather than to some other property of the sample.

Note that, by definition, individuals in the comparison group consist of offenders whodid not make transitions to stable employment. This presents a challenge for modelingthe timing of change in criminal offending relative to the point of job entry as there wasno such event in the comparison group. To overcome the challenge, each individual in thecomparison group was assigned a randomly generated date (from a uniform distribution)of quasi-employment to simulate the point of entry to an actual job. This approach, whichis sometimes described as a “placebo test” (Rosenbaum, 2002: 205), allows us to modelthe timing of change among those who did not get a job in reality. The key point is thatwe should not expect simulated job entries to be related to changes in criminal activity.In addition to meeting the general sampling criteria described in figure 2, members of the comparison group were individually matched to the employment group with respect

4. By way of comparison, 50 percent of the median household income—a common standard forpoverty line—was NOK 78,000 in 2000.




to age, marital status, and criminal history (felony convictions and incarcerations). Themethodology and results of the matching procedure are described in more detail in thefollowing sections.

MEASURESBased on police data on solved cases, criminal offending is measured with a dummy

indicating the occurrence of at least one offense in a given month (1 = yes, 0 = no). Weincluded incidents classified as felonies, leaving out misdemeanors consisting primarilyof shoplifting and traffic violations. Although these data reflect offenses resulting in acriminal justice sanction, the information on timing is based on the date of the offense,not on the date of the sanction.

Monthly data on employment status were obtained from the national employee registry,which documents all employer–employee relationships in the private and public sector.To address entrepreneurial arrangements, we used the tax registry to exclude from thesample anyone who had income from self-employment above NOK 70,000 in any of theyears 1998–2000. Monthly employment status is measured as a dichotomy (1 = yes, 0 =

no) indicating employment status on the first day of each month.Our control variables include age, period (in annual units), and season of the year

(winter, spring, summer, and fall). We also control for monthly marital status by includingtwo dummies: married (1 = yes, 0 = no) and divorced (1 = yes, 0 = no).

TIME AT RISK

The data were weighted to adjust for individual differences in the time at risk to reof-

fend. This weight reflects the proportion of each month the person was alive, resided inNorway, and was living in the community (i.e., not in prison). For example, a person whodied on September 15 would be assigned the value 15/30 = .50 for that month and zero foreach subsequent month. The average time at risk is stable throughout the observationalperiod and equals 28.6 days per month.5 To ensure that each subject contributed obser-vations to preemployment and postemployment trajectories, the employment sample andthe comparison group were limited to individuals residing in Norway for a minimum of 12 months before and after the employment transition.

METHODS OF ESTIMATIONREGRESSION WITH SMOOTHING SPLINES

The data conform to a panel structure in which persons are observed up to 36 monthsbefore and after the employment transition. To capture changes in the offending rate rel-ative to the point of job entry, we created a variable TIME to denote the number of

5. The accuracy of records measuring length of incarceration varies across the observational period.Exact information on the number of days spent in prison is not available until 2001. To assess

the consequences of this limitation, we reexamined the data excluding individuals sentenced toincarceration during 1998–2000, and we focused on employment events taking place in 2002 orlater. The results from these analyses replicated the pattern observed in the full sample. Runningthe same models with and without weights did not alter substantive results.




months before/after the month of the employment event. Depending on the date of jobentry during the observational period, TIME ranges from −36 to +36. Those who becameemployed in December 2006 contribute a maximum of 12 months to the postemploymentperiod, as 2007 is the final year of tracking. The ages at the time of job entry range from

27 to 46, and the age distribution over the full observational period ranges from 24 to 47.The parameters of the TIME variable capture the probability of offending in that par-ticular month. To estimate monthly changes in offending with maximum accuracy, wedid not want to impose a particular functional form (e.g., linear or curvilinear) on theTIME variable. Accordingly, TIME is specified as a nonparametric smoothing spline inthe framework of generalized additive models (GAMs), which is an extension of gener-alized linear models in which the linear predictor involves a sum of smooth functions of selected covariates while controlling for additional variables (Wood, 2006). Because thismethod has not been widely used in criminology, a brief explanation is in order. The nextparagraph is based on Wood (2006).

Consider a model that contains only one smooth function of a single covariate: yi = f ( xi ) + εi , where y is the outcome variable, x is a covariate with a smooth function f ,and ε is the error term. To estimate this equation, f is specified as a linear model. Themost basic way to specify the smooth function is to define f as a cubic spline; in whichcase, we would say the smooth has a polynomial basis. Under this approach, f ( x) is di-vided into sections of cubic polynomials connected so that they become continuous up tosecond derivatives. One disadvantage with the polynomial splines method is the need tomake arbitrary decisions about 1) the smooth basis, 2) where the polynomials (known asknots) should join, and 3) how many knots there should be. To overcome this limitation,we use thin plate regression splines as an alternative smooth basis. Under this approach,

the “wiggliness” of the spline is penalized as a trade-off between model fit and smooth-ness in the estimation.6 A key characteristic of nonparametric smoothing splines is thatthey do not have a simple functional form with parameters to interpret. For example, noslope parameters are available to indicate the average change in the outcome by one unitchange in the predictor. The results from GAMs are therefore usually presented as plotsof predicted values.

GAMs are an extension of the generalized linear model that can be extended further togeneralized additive mixed models (GAMMs) for correlated observations. Because ourdata have a panel structure with correlated observations, we specify a random interceptmodel to ensure correctly estimated standard errors. Appropriate for the current study,

we specify the models so that the outcome y (a dummy for person i having committed acrime at time t ) is expressed as a linear probability model.

yi t = ηi + X ∗i t θ + f (TIMEi t ) + εi t (1)

where X represents a vector of parametric terms with corresponding parameters θ ,which in this study is calendar age, marital status, calendar year, and the season of theyear; and f is a smoothing function over the covariate TIME. The term ηi refers tothe individual-specific intercept following a normal distribution with variance σ to be

6. Explaining the details of thin plate splines goes beyond the scope of this study. Interested readersshould consult chapters 3 and 4 in Wood (2006).




estimated.7 Our analysis is focused on estimating the TIME function. The results are pre-sented as plots of predicted values at the sample mean for all covariates.

Although a logit specification is the standard choice for binary outcomes, we use alinear probability model (LPM) specification for practical reasons. Because estimating

logit models with nonparametric smoothing splines in combination with random effects iscomputationally very demanding, we experienced instabilities and failed convergence inour models. We did not encounter any such issues using linear probability specifications.Because the two methods have been shown to yield practically identical predicted meanprobabilities, LPM seems like a prudent option (Angrist and Pischke, 2009: 197). 8

Allowing Disjunction at Job Entry

A single smoothing term does not allow for a shift in offending rate at the month of jobentry. Failing to attend to this issue potentially could conceal an observable reduction incriminal offending occurring at this particular transition point. To address this limitation,we respecify the model as follows to allow discontinuity in the pattern

yi t = ηi + X ∗it θ + di t β + f 1 (TIMEi t ) + f 2 (TIMEi t ) × di t + εi t (2)

where d is a dummy variable with value 0 before job entry and 1 after job entry. Thisspecification allows the spline function to be fitted separately for the two time periods,with coefficient β capturing the amount of difference at the point of separation.

Incorporating the Comparison Group

The comparison group is incorporated in the analysis by allowing the smoothing splineto vary across the two groups while holding all controls constant across groups. The modelthen becomes

yi t = ηi + X ∗it θ + COMPiβ + f 1 (TIMEi t ) + f 2 (TIMEi t ) × COMPi + εi t (3)

where COMP is a dummy [0,1] for membership in the comparison group with parame-ters β . This specification enables the estimation of separate curves for the treatment andcomparison groups.

Models 2 and 3 can be combined to allow for a shift in offending at the point of job entryin each group. We let C be a factor with four values for each combination of membershipin the comparison/employment group and before/after job entry. The final model for ourmain analysis thus becomes

yi t = ηi + X ∗it θ + C i t β + f 1 (TIMEi t ) + f 2 (TIMEi t ) × C i t + εi t (4)

There is no reason to expect a shift in offending at the month of entry to quasi-

employment (in the comparison group) because this not an actual event. However, tomake sure this assumption is correct, we first estimate a model that tests for the presence

7. The models are estimated using R (R Core Team, 2012) and the package gamm4 (Wood, 2011).8. To ensure our results are not affected by the choice of link function, we ran computationally less

intensive models comparing logit and LPM specifications. As expected, the predicted mean prob-abilities were practically the same (results are available from authors).




of a statistically significant disjunction in the comparison group before specifying a modelthat allows C to vary only in the employment group.

Conditioning on Continued Employment

Although everybody in the employment sample got a job, not everybody remainedemployed over the 36-month period. As an additional aspect of our research, we exam-ine the consequences of continued employment versus job loss on criminal offending. Weconstruct a variable JOBLOSS that takes the value 0 up to TIME = 6 and 1 if no longeremployed in the period after the 6-month period that defines the initial job entry. Notethat we do not include the comparison group in this analysis because individuals in thisgroup are not at risk of losing their “jobs.”9 We calculate the predicted probabilities con-ditioning on JOBLOSS = 0 from a regression model described in the following equation:

yi t = ηi + X ∗it θ + di t β1 + β2JOBLOSSi t + f 1 (TIMEi t ) + f 2 (TIMEi t ) × di t + εi t (5)

Age-Graded Patterns

Prior research and theory suggest that the influence of employment on desistance mayvary by age. We address this hypothesis by estimating a model that includes an interactionterm between age and TIME. Because age ranges from 24 to 47 years of age, this yields agrid surface of 24 age groups and 73 time points. When smoothing the surface, the relevantmodel becomes

yi t = ηi + X ∗it θ + f (TIMEi t × AGEi t ) + εi t (6)

Group-Based Patterns

It is conceivable for each theoretically proposed trajectory (see figure 1) to commandmeaningful presence in the data. Because this outcome would not be captured by amethod constrained to estimate a single trajectory, we engage the semiparametric group-based modeling (SPGM) approach to construct latent trajectory groups (Nagin, 2005; Na-gin and Land, 1993). Under SPGM, the time-related variable is specified as a polynomial

up to a fourth degree, which varies over the latent classes. The number of latent classesis determined by comparing alternative models using the Bayesian information criterion(BIC) to decide on the best-fitting model. The model can be expressed by the equation

logit ( yi t ) = β g0 + f g (TIMEi t ) (7)

where the superscript g denotes the latent classes and f (TIME) is a polynomial functionof the time-related variable. Conditioning on the model, each person’s (posterior) like-lihood of belonging to any given group g can be calculated as a function of his or herbehavioral profile (Nagin, 2005: 78–83). Once the latent classes have been established,

9. The comparison group is not featured in any of the remaining models given its limited analyticpurpose.




class membership can be treated as a fixed individual characteristic in any further analysis(Roeder, Lynch, and Nagin, 1999). As an important departure from typical applicationsof this method, we use SPGM strictly as a heuristic device to identify the most commonlyoccurring trajectories. We make no claim that these groups represent truly distinct groups;

the true latent distribution might very well be continuous (Skardhamar, 2010). Becausewe are interested in capturing heterogeneity in the pathways leading up to the employ-ment, we fit SPGM only for the 36 months preceding the employment event, clusteringpersons with similar recent preemployment offending trajectories.

After having assigned each person to the appropriate latent class (based on posteriorprobabilities), we estimate a new model using smoothing splines while treating the latentclasses as fixed. This application of the method allows the latent classes to determine pre-employment variation in offending trajectories while letting the postemployment patternsvary freely (nonparametrically) across latent classes. The model then becomes

yi t = ηi + X

∗

it θ + Giβ + f (TIMEi t ) × Gi + εi t (8)where G is the assigned latent class. In short, this approach yields the average offendingtrajectory for the entire observational period conditional on a person’s criminal careerprior to job entry.

MATCHING PROCEDURE FOR COMPARISON GROUP

The analytic step described in equation 4 involves a matched comparison group consist-ing of those who did not make transitions to stable work but who are otherwise similar toindividuals in the employment group. Although propensity score matching is frequently

described as a method for estimating causal effects (e.g., King, Massoglia, and MacMil-lan, 2007), it is important to understand that, limited to observed covariates, it can onlyalleviate but not eliminate selection bias (see, e.g., Morgan and Winship, 2007: 122). Asexplained, the sole purpose of including a comparison group in our research is to addressthe possibility that changes in offending observed in the employment group are driven bythe composition of the sample rather than by the employment experience.

Instead of the commonly used technique of nearest-neighbor propensity score match-ing (Rosenbaum and Rubin, 1983), we use a newer algorithm known as genetic matching(Diamond and Sekhon, 2013) that automates the iterative checking and improvement of balance (Sekhon, 2011). Generalizing propensity score and Mahalanobis distance match-

ing (Diamond and Sekhon, 2013; Sekhon, 2011), genetic matching searches the space of distance metrics to find the optimal solution. As stated, our samples were matched toachieve balance on age, timing of job entry, marital status, and criminal history. Criminalhistory was captured with two variables: felony conviction rate and imprisonment dummy,each of which covers outcomes occurring in the 3 years preceding the onset of the obser-vational period (TIME = −36). In addition, the pool of eligible matches was limited toindividuals meeting the general sampling criteria described in figure 2, with the obviousexception of job entry.

The purpose of matching is to achieve covariate balance, defined as similarity in the dis-tribution of covariates between the groups (Diamond and Sekhon, 2013). Thus, to eval-uate balance, it is important to use measures that compare the distribution as well as themean values of the covariates. Following Imai, King, and Stuart (2008), we compared thevalues of each variable quantile by quantile and report the mean absolute differences over




Table 1. Descriptive and Balance Statistics in the Employment andComparison Groups Before and After Matching

Indicators of Balance Before After

Age at t = −36 Mean employment group 36.9 36.9Mean comparison group 38.6 36.9Mean eQQ difference years 1.7 .1Variance ratio × 100 101.6 102.4

Married at t = −36 Percentage employment group 15.3 15.3Percentage comparison group 6.2 15.3Mean eQQ difference percentage points 9.1 .0Variance ratio × 100 223.2 100.0

Divorced at t = −36 Percentage employment group 12.5 12.5Percentage comparison group 13.3 12.4Mean eQQ difference percentage points .8 .1Variance ratio × 100 94.9 100.9

Date of job entry Mean eQQ diff days 166.8 20.7

Variance ratio×

100 117.1 105.6Number of felonies, pretracking Mean employment group 9.1 9.1Mean comparison group 9.9 8.9Mean eQQ difference number of crimes 1.6 .5Variance ratio × 100 151.1 103.2

Incarcerated (%), pretracking Percentage employment group 48.7 48.7Percentage comparison group 51.1 48.8Mean eQQ difference percentage points 2.4 .1Variance ratio × 100 100.1 100.0

N Employment group 783 783Comparison group 3,513 752

ABBREVIATION : Mean eQQ = mean difference over the empirical quantile–quantile comparisons.

these quantiles (eQQ). This statistic equals zero if the distribution is perfectly balancedbetween the two groups; a positive value indicates the amount of imbalance on the naturalscale of the variable in question. We also report variance ratios indicating the differencein estimated variances.

RESULTS

DESCRIPTIVE STATISTICS

Table 1 reportsdescriptive statistics for the employment and comparison groups at thebeginning of the tracking (t = −36) before and after matching. The average age of themen in the employment sample was 36.9 years. Approximately 15.3 percent were mar-ried and 12.5 percent were divorced. The dates of job entry were evenly distributed (notshown) across the observational period. In the 3 calendar years before tracking, the aver-age number of felony offenses was 9.1. Note that the average number of convictions is lessbecause one conviction may include multiple offenses. Approximately half of the sample(48.7 percent) had a recent (unconditional) prison sentence.

Table 1 also shows the results from the matching procedure. The comparison grouphas similar average values on most of the covariates before matching and only minor dif-ferences in distributions. In light of prematching statistics, the two groups were primar-ily dissimilar with respect to proportion married and the distribution of previous crimes.For example, the average number of felonies was virtually identical (8.9 vs. 9.1), but the




variance was 1.5 times larger in the employment group and the average difference overthe quantile-quantile comparisons was 1.6. After matching, these and other signs of mean-ingful imbalance disappeared, suggesting an excellent match between the employmentand comparison groups.

Panel A of figure 3 describes the unadjusted (except for time-at-risk) monthly offend-ing rates before and after job entry (in the employment group). Although we observemonthly fluctuations, the overall trend shows systematic decline prior to job entry, fol-lowed by a leveling off during the posttransition period. Panel B of figure 3 describes themonthly employment rate. Because “job entry” was defined as becoming employed for aminimum of 6 consecutive months, shorter employment spells may (and do) occur priorto the transition of analytic interest. Data for the postemployment period (TIME > 0)indicate that most subjects lost their jobs after the 6-month period. By the end of tracking(TIME = 36), 38 percent of the men remained employed. Note that these statistics includeemployment in the initial job as well as any other employment that may have occurred

during the observational period.

GENERAL PATTERNS

Figure 4 displays the predicted probabilities of monthly offending rates for the employ-ment and comparison groups as specified in equation 4. For the employment group, weallow for a shift in offending at the month of job entry. As the related term was not sig-nificant ( p = .28) in the comparison group (as expected), we removed it from the model.(The shift was not statistically significant in the employment group either [ p = .64], butwe kept the term in the model for substantive reasons.) The coefficients and fit statistics

pertaining to spline regression models are provided in table S.1 of the online supportinginformation.10

The trajectory associated with the employment group shows a clear and monotonousdecline in the offending rate in the period leading up to job entry, followed by an almostequally strong rebound in subsequent months. By the end (t = 36), the posttransitioncurve seems to have stabilized at a level that is only marginally lower than the offendingrate at the beginning of the observational period (t = −36). The pattern for the compari-son group is markedly different. In clear contrast to the employment group, the trajectoryis essentially flat. This result is important because it rules out the possibility that the pat-tern associated with the employment group reflects a generic process related to the crim-

inal history or some other compositional aspect of the sample. The main conclusion fromfigure 4 is that employment entries are preceded by substantial reductions in offendingwith no further gains after the employment transition.

The trajectory for the employment group in figure 4 includes those who remained em-ployed after the required 6-month period as well as those who lost their jobs. As re-ported in figure 5, the postemployment pattern looks different under the assumption of continued employment (see equation 5). Note that figure 5 does not include the com-parison group; the members of this group were not at risk of losing their (fictional) jobs.

10. Additional supporting information can be found in the listing for this article in the Wiley OnlineLibrary at http://onlinelibrary.wiley.com/doi/10.1111/crim.2011.52.issue-2/issuetoc.




F i g u r e

3 .

D e s c r i p t i v e S t a t i s t i c s o f O f f e n d i n g a n d E m

p l o y m e n t O u t c o m e s

M o n t h s b e f

o r e / a f t e r j o b e n t r y

P r o p o r t i o n

− 3 6

− 3 0

− 2 4

− 1 8

− 1 2

− 6

0

6

1 2

1 8

2 4

3 0

3

6

0 . 0 0 0 . 0 2 0 . 0 4 0 . 0 6 0 . 0 8 0 . 1 0

( a )

P r o p o r t i o n c o m m i t t i n g a c r i m

i n a l o ff e n s e b y m o n t h ( u n a d j u s t e d )

M o n t h s b e f o r e / a f t e r j o b e n t r y

P r o p o r t i o n

− 3 6

− 2 4

− 1 2

− 6

0

6

1 2

1 8

2

4

3 0

3 6

0 . 0 0 . 2 0 . 4 0 . 6 0 . 8 1 . 0

( b ) P r o p o r t i o n

e m p l o y e d b y m o n t h ( u n a d

j u s t e d )




Figure 4. Predicted Probabilities of Offending by Month Before/AfterJob Entry

Months before/after job entry

P r o b a b i l i t y

−36 −30 −24 −18 −12 −6 0 6 12 18 24 30 36

0 . 0

0

0 . 0

2

0 . 0

4

0 . 0

6

0 . 0

8

0 . 1

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4

Comparison group

Employment group

The probabilities reported in figure 5 were estimated under the assumption of contin-ued employment over the entire observational period after job entry (36 months). Thus,the only difference between figures 4 and 5 is the postemployment trajectory. Under theassumption of continued employment, the “rebound effect” observed in figure 4 disap-pears, which implies it is related to increased offending among those who became un-employed. Note, however, that it would be inappropriate to conclude that the rebound

was caused by unemployment because we cannot rule out systematic selection or reversecausality. Perhaps job loss occurred as a result of criminal activity, or perhaps individualswho lost their jobs were different from those who kept their jobs with respect to criminalpropensity.

We find it informative that the offending rate in figure 4 begins to increase immediately

after job entry rather than 6 months later, which is when job losses begin to accrue. If the rebound effect were caused by job loss, then it should not emerge until after the 6-month mark. Thus, it seems more plausible to conclude that the flat postemploymenttrajectory in figure 5 reflects a selection effect, but we cannot rule out the possibility thatcontinued employment helps keep these high-risk men from returning to their offendingways. In conclusion, the evidence from figures 4 and 5 shows that employment entriesare preceded by substantial reductions in criminal offending with no further reductionsduring the postemployment period.




Figure 5. Predicted Probabilities of Offending by Month Before/AfterJob Entry, Conditional upon Continued Employment



−36 −30 −24 −18 −12 −6 0 6 12 18 24 30 36

0 . 0

0

0 . 0

2

0 . 0

4

0 . 0

6

0 . 0

8

0 . 1

0

0 . 1

2

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4

Employment group

AGE-GRADED PATTERNS

We now let the offending trajectory vary by age as specified in equation 6. Figure 6adisplays the smoothed grid surface of predicted probabilities in a three-dimensional plot.The base of the plot consists of the AGE-axis on the left side and the TIME-axis on theright. Note that the TIME-axis is coded in the “backward” direction for optimal visualiza-tion of the patterns and that results are based on the assumption of continued employment

across the full tracking period.To read the chart, focus first on those at 47 years of age—the oldest group in the sample.

Their trajectory is represented by the line at the front edge of the plot, stretching from theupper right corner down to the front-facing corner (the corner between “age = 45” and“time = 30”). At time −36, their offending rate equals approximately 5.1 percent, declin-ing rapidly toward the month of job entry (TIME = 0) at 1.6 percent. The posttransitiontrajectory continues to decline, and by the end of tracking (TIME = 36), the offendingrate is approximately .6 percent. If we compare this result with the pattern observed forthose 30 years of age or younger, then we observe some evidence of an age interaction inthe postemployment period: It seems that continued employment is not associated withsubsequent reductions in criminal offending among the younger males in the sample. Theage trends for the preemployment period are more similar, although the rate of declineseems to grow somewhat larger with age.




F i g u r e

6 .

P r e d i c t e d P r o b a b i l i t i e s o f O f f e n d i n g b y M

o n t h B e f o r e / A f t e r J o b

E n t r y b y A g e ,

C o n d i t i o n a l

u p o n C o n t i n u e d E m

p l o y m e n t

M o n t h s

b e f o r e / a f t e r j o b e n t r y

− 3 0

− 2 0

− 1 0

0

1 0

2 0

3 0

A g e

2 5

3 0

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4 5


0 . 0

0

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0 4

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8

( a ) 3 D p l o t o f T I M

E x A G E s u r f a c e

M o n t h s b e f o r e / a f t e r j o b e n t r y


− 3 6

− 2 4

− 1 2

0

1 2

2 4

3 6

0 . 0 0 0 . 0 2 0 . 0 4 0 . 0 6 0 . 0 8

( b ) L i n e p l o t f o r e a c h o n e - y e a r a

g e g r o u p ( l i n e s g e t l i g h t e r i n

c o l o r a s

a g e g e t s o l d e r )




Figure 7. Predicted Probabilities of Offending by Month Before/AfterJob Entry. Smoothed Trajectories from 5-group SPGM



Group 1 ( 70.9 %)

Group 2 ( 12.7 %)

Group 3 ( 11.2 %)

Group 4 ( 3.3 %)

Group 5 ( 1.9 %)

−36 −30 −24 −18 −12 −6 0 6 12 18 24 30 36

0 . 0

0 . 1

0 . 2

0 . 3

0 . 4

0 . 5

0 . 6

For an improved visualization of the age-specific patterns, figure 6b presents criminaltrajectories for each 1-year age group in a two-dimensional plot. Although this dense im-age is not helpful for describing any single age group, it highlights the overall conformityin the patterns. The declining trends tend to be sharper among the older groups (the lowerpart of the figure), but the results are similar with respect to the timing of change. Regard-less of age, employment entries are preceded by substantial reductions in offending withmodest or no additional reductions after the employment transition.

GROUP-BASED EFFECTS

Although it now seems that most offenders in our sample had desisted before theymade transitions to stable jobs, it remains possible that a meaningful number of offendersdeviate from this general pattern. To address this possibility, we estimated SPGMs toconstruct latent trajectory groups for the 36 months preceding the employment event. Thepurpose of this approach is twofold: 1) to examine variation in preemployment criminaltrajectories and 2) to determine whether the consequences of employment for desistancediffer depending on the timing of job entries in the criminal career.

The main analytic challenge in SPGM is to settle on the correct number of latent classesto represent the data. This task is usually accomplished by comparing models with an in-creasing number of latent classes and by examining their relative fit using BIC (Nagin,2005). Based on the BIC values, the five-group solution presented in figure 7 emerged




as the best-fitting model in these data. We present results from a third-degree polyno-mial in the model search (using a fourth-degree polynomial gave similar results). Keep inmind that these five trajectories were generated using a two-stage modeling strategy: The preemployment trajectories represent the latent classes from the best-fitting group-based

model as described earlier, whereas the postemployment trajectories were estimated assmoothing splines specific to each latent class (see equation 8 and the online supportinginformation for details).

Covering 71 percent of the sample, group 1 represents the most typical pattern by far.The essentially flat trajectory associated with this latent class implies low offending levelsbefore and after the employment transition. It seems that individuals in this dominantgroup had desisted well ahead of job entry. With the data at hand, we cannot determinewhen this change occurred, but because the sample includes only those with a fairly in-tense criminal history (see figure 2), we do know that these men had been active offendersin the not-so-distant past.

Group 2 is the next largest, covering 13 percent of the sample. Individuals in this la-tent class experienced a sharp reduction in criminal offending during the first half of thepreemployment period, followed by a stable trend of low offending. It is notable that thestabilization occurs 12 months prior to job entry and is not associated with subsequentreductions in the postemployment period. Groups 3 and 4 are variations on a theme: Ineach situation, the initial preemployment offending rate fluctuates at a relatively highlevel, and in both cases, the employment transition is preceded by a sharp and steadydecline in criminal activity. In group 4 (3.3 percent of the sample), the peak offendinglevel is higher and the downward slope starts earlier than in group 3 (11.2 percent of thesample).

Finally, group 5 represents the most distinct and marginal pattern (only 1.9 percent of the sample). This latent class is associated with the highest average offending rate duringthe preemployment period, and although we observe some reduction in criminal activityimmediately prior to job entry, the level of offending remains comparatively high. More-over, in sharp contrast to the other groups, the postemployment period is characterizedby a robust continuation of the declining trend. The trajectory associated with group 5 isthe only one consistent with the expectation of causal influence of employment on desis-tance. Depending on how much theoretical weight is assigned to the fact that the timingof desistance predates job entry (by less than 6 months), this pattern is consistent witheither the hook-for-change or the turning point hypothesis.

ROBUSTNESS CHECKS

Sampling Criteria

Our results are based on very specific sampling criteria that, although grounded in the-ory, may be considered arbitrary. For example, one might challenge our operationaliza-tion of “stable” employment or “serious” criminal history. Changes to these criteria wouldobviously alter the sample with the potential of affecting results. To examine the sensitiv-ity of our findings to sampling decisions, we repeated our main analysis (see figure 4) withalternative samples using varying definitions of criminal history and labor market status.Figure S.1 in the online supporting information presents the results from four samplespecifications that differ from the main analytic sample as follows:




a. We emphasized the intensity of recent of criminal activity by requiring at least five(as opposed to only one) offenses during the period 1998–2000.

b. We restricted the definition of “marginal labor market status” to include employ-ment spells of only 3 months or less (as opposed to 6 months or less) since 1998.

c. We changed the definition of stable employment from 6 months to 12 months.d. We dropped the requirement of “frequent offending” and included anyone withat least one felony-level offense since 1998.

For simplicity, these models were fitted without allowing for a shift in offending at themonth of job entry. Addressing discontinuity made no difference to these results (or theresults reported in the main analysis). For similar reasons, the comparison groups used inthe supplementary analyses were not matched individually with the employment groups.Instead, they were drawn from the total population of eligible individuals using uniformsampling criteria (with the exception of job entry). The patterns reported in figure 4 aresimilar without matching (results available from the authors).

As reported in figure S.1, the offending patterns are uniform across the four alterna-tive sample specifications. The declining trend preceding the employment transition isobserved in each version of the employment group, and so is the rebound effect after jobentry. In the comparison groups, the rates of offending remain stable and elevated acrossthe board. Only the levels of offending vary somewhat between the samples, as one wouldexpect given that they differ with respect to the intensity of criminal history. These resultssuggest that our findings are robust and are not an artifact of any specific decision madein the process of selecting a sample of crime-prone men making a transition to legitimatework.

Employment Simulations

Given the random assignment to quasi-employment in the comparison group, our mainresult (as presented in figure 4) is but one of many possible outcomes. To examine therobustness of this finding, we repeated the assignment of job entries and performed theanalysis 50 times using the same specifications as in the main analysis. Figure S.2 in the on-line supporting information plots the predicted probabilities across all repetitions for thecomparison group. The thicker line in the middle describes the average of these values.The line associated with the employment group is the same as in figure 4. Consistent withthe original result, the pattern for the comparison group does not decline but stays es-

sentially flat over the entire observational period. The line averaged over the repetitionsapproximates a straight line, indicating a stable offending rate in the comparison group.This finding adds confidence to the conclusion that the patterns observed in the employ-ment group reflect processes related to the employment transition and are not generatedby some auxiliary characteristic of the research design.

DISCUSSION

The research program launched by Sampson and Laub (1993) has elevated the topicof desistance from relative obscurity into one of the more vibrant areas of criminologicalinquiry of the past 20 years. As a formal acknowledgment of this influential work, Samp-son and Laub received the 2011 Stockholm Prize in Criminology. Reading from the state-ment by the international jury, the prize was awarded “for research showing why and how




criminals stop offending.” Specifically, “Laub and Sampson discovered that even very ac-tive criminals can stop committing crimes for good after key turning points in their lives”(The Stockholm Prize in Criminology, 2011: 5). As a modification of the turning pointhypothesis, the theory of cognitive transformation argues that objective changes in life

circumstances may contribute to desistance as long as they are preceded by an internal-ized commitment to change (Giordano, Cernkovich, and Rudolph, 2002). The purpose of the current study was to evaluate the capacity of employment to exert influence consistentwith these assumptions.

Extensive prior research has focused on documenting average offending rates beforeand after the employment transition. Although the evidence is mixed, several sophis-ticated studies reported reduced levels of offending during states of employment. Animportant shortcoming of those studies, however, is that they did not address the criti-cal issue of time order with sufficient precision. For employment to influence offending,changes in offending must occur after the employment event. If desistance from crime has

occurred prior to job entry, then employment should not be treated as a causal factor butas a consequence of “going straight.”

Using monthly data from Norway, this research examined criminal trajectories of alarge sample of serious repeat offenders who became employed in a job lasting a minimumof 6 months. The results from the analyses show that, for most offenders, job entry ispreceded by significant declines in criminal activity. In light of these data, it seems to beextremely rare for offenders to transition to stable employment during an active phase of the criminal career. Importantly, as described in the online supporting information, thesefindings are robust across varying sample definitions.

The most typical path to employment involves a period of criminal inactivity lasting 2

years or more. This result is consistent with the assumption that a significant reorientationin life priorities is a necessary precondition for voluntary job entry among men with anextensive history of criminal offending. In this regard, our results contradict the turningpoint hypothesis and cohere with the maturation perspective and the hook-for-change hy-pothesis, both of which assume individual change prior to job entry. The main differencebetween these two accounts has to do with the consequences of employment for the crim-inal trajectory: The hook-for-change hypothesis predicts further reductions in offending,whereas the maturation perspective does not.

Results from the analysis of postemployment offending rates showed that the shape of the trajectory depends on whether the person remains employed. The trajectory for the

full sample, in which most individuals lost jobs during the observational period, showedan increase rather than a decrease after the point of job entry. This finding suggests that,on average, having a job lasting a minimum of 6 months does not contribute to desis-tance from crime. The results from the model assuming continued employment showedno change in offending during the postemployment period. Although it is possible thatkeeping the job prevented these crime-prone men from offending, the timing of the “re-bound effect” observed in the full sample points to systematic selection as a more plau-sible explanation.11 Most critically, no evidence of reduced offending was found after thetransition to legitimate work under either specification. We found some support for the

11. If job losses were a factor, we should not have observed increases in offending until 6 months afteremployment entries given that everybody in the sample remained employed until that point.




expectation that older offenders benefit from employment more than younger offenders.Under the assumption of continued employment, the post-transition offending trajecto-ries continued to decline somewhat among offenders around 40 years of age and older.However, in each age group, reductions in criminal activity preceding employment were

far more substantial than any declines that followed.The results from group-based models present a more nuanced picture of postemploy-ment criminal trajectories. In light of these patterns, most individuals experience nomeaningful reductions in criminal activity after employment; the evidence consistent withcausal influence is limited to a group of offenders that represents less than 2 percent of the sample (group 5). Although this result cannot be generalized beyond the sample usedin the analysis, it is worth noting that the sample was selected in close adherence with thescope conditions of the turning point hypothesis.

Perhaps our failure to find more substantial support for the employment effect hasto do with the inability of the data to distinguish between “good” jobs and “bad” jobs.

After all, the age-graded theory does not claim that any job will automatically serve as aturning point (Sampson and Laub, 1993). Like marriages, jobs are expected to influencethe desistance process only insofar as they build stakes in conformity and assist in theformation of prosocial identities (Laub and Sampson, 2003). In our defense, the theorydoes not provide clear guidelines for measuring job characteristics. “Stability” is the mostcommon attribute used to identify jobs with potential to reorient criminal trajectories.Qualitative case studies also highlight the importance of a steady paycheck. Our decisionto focus on jobs lasting for a minimum of 6 months was informed by these specifications.In addition, key studies in this literature focus on within-individual changes in states of employment, ignoring variation in job characteristics (Horney, Osgood, and Marshall,

1995; Laub and Sampson, 2003; Piquero, MacDonald, and Parker, 2002; Uggen, 2000).Because our goal is to contribute to this literature, it seems appropriate to use comparablemeasures. (As reported under robustness checks, the results were similar even when wefocused on jobs lasting 12 months or longer.)

Perhaps the pattern of results observed in this research is unique to Norway or, moregenerally, to Nordic welfare states. The presence of a strong social safety net and com-paratively lenient treatment of convicted offenders (Pratt, 2008; Savolainen, 2009) maydecrease incentives for employment among individuals drawn to a criminal lifestyle. Thelow likelihood of job entry before the decision to stop offending may reflect this macroso-cial context. It is possible that the processes described in the turning point hypothesis are

more common in countries like the United States where socioeconomically marginalizedmen do not have the “luxury” of prolonged disengagement from the labor market (un-less they remain active in the illegal economy). In a social system of the latter variety,crime-prone men may be more likely to accept jobs at initial stages of criminal desistancebecause they need the money to survive or because it is a required condition of parole.

The only way to address this issue is to replicate the analysis with data from othercountries. While awaiting such contributions, we find it reassuring that our data behaveconsistently with patterns observed in prior research conducted in a variety of nationalsettings, including Finland (Savolainen, 2009), the Netherlands (van der Geest, Bijleveld,and Blokland, 2011), and various jurisdictions of the United States (Laub and Samp-son, 2003; Wright and Cullen, 2004). Similar to those studies, we find average offending

rates to be lower in the postemployment period (see figure 5). Our findings are consistentwith qualitative and experimental studies that showed no support for the assumption that




employment plays a major role in triggering desistance from crime (Bushway and Apel,2012; Giordano, Cernkovich, and Rudolph, 2002; Maruna, 2001).

Given the focus of this research on employment, the results cannot be generalized toother potentially salient life events, including marriage, parenthood, or residential change

(Kirk, 2012; Kreager, Matsueda, and Erosheva, 2010; Sampson, Laub, and Wimer, 2006).Thus, it remains possible that many of the men in our sample were able to capitalize onany number of “tangible hooks” as a part of the desistance process. It should be clear,however, that the issue of timing is equally critical in studies of these kinds of transitions.For example, to demonstrate a marriage effect on desistance, it is necessary to show re-ductions in offending after the date of marriage. Consistent with our results on employ-ment, recent research by Lyngstad and Skardhamar (2013) suggested that marriage ismore likely to follow desistance than vice versa. They found no evidence of additionalreductions in crime after transitions to marriage.

As an advanced welfare state, Norwegian society provides comprehensive systems of

support for socioeconomically marginalized individuals. Although outside the scope of the current study, we find it reasonable to hypothesize that reliance on nonpunitive pe-nal policies, universal access to high-quality treatment services, and the omnipresence of opportunities for adult educational and training programs make it easier for crime-pronemen in Norway to reintegrate into the economic mainstream. Future research should ex-amine whether any of these factors play a role in redirecting lives away from criminalactivity and toward stable employment. In short, our research does not explain why thesemen desisted from crime, only that employment entries do not seem to have played ameaningful causal role in this process.

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Torbjørn Skardhamar is a senior researcher at the Research Department of StatisticsNorway. His work is focused on life-course criminology, statistical methods, and the useof register data in social research. Currently, he is investigating spatial patterns of crimein Oslo.

Jukka Savolainen is an associate professor of criminology at the University of Nebraskaat Omaha. His areas of interest include life-course criminology, cross-national studiesof delinquency, and situational aspects of violence. Currently, he is writing a paper on

revenge-motivated offending.

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Timing of Employment and Desistance