Work as Turning Point

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Work as a Turning Point in the Life Course of Criminals: A Duration Model of Age,Employment, and RecidivismAuthor(s): Christopher UggenSource: American Sociological Review, Vol. 65, No. 4 (Aug., 2000), pp. 529-546Published by: American Sociological AssociationStable URL: http://www.jstor.org/stable/2657381 .

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WORK AS A TURNING POINT INTHE LIFE COURSE OF CRIMINALS:A DURATION MODEL OF AGE,EMPLOYMENT, AND RECIDIVISMCHRISTOPHER UGGENUniversity of Minnesota

Sociologists have increasingly emphasized "turning points" in explaining behav-ioral change over the life course. Is work a turning point in the life course of crimi-nal offenders? If criminals are provided with jobs, are they likely to stop committingcrimes? Prior research is inconclusive because work effects have been biased byselectivity and obscured by the interaction of age and employment. This study yieldsmore refined estimates by specifying event history models to analyze assignment to,eligibility for, and current participation in a national work experiment for criminaloffenders. Age is found to interact with employment to affect the rate of self-reportedrecidivism: Those aged 27 or older are less likely to report crime and arrest whenprovided with marginal employment opportunities than when such opportunities arenot provided. Among young participants, those in their teens and early twenties, theexperimental job treatment had little effect on crime. Workthus appears to be aturning point for older, but not younger, offenders.

L IFE-COURSE transitions, such as en-try into marriage or employment, maybe "turningpoints" (Elder 1985) in the livesof criminal offenders (Sampson and Laub1993; Warr 1998). Because researcherstypi-cally cannot assign people to these statuses,however, it is difficult to determinewhethersuch transitions are causes or correlates ofchanges in offending. In fact, early or "pre-cocious" transition to these adult roles ap-pearsto worsen problembehavior (Bachman

and Schulenberg 1993), suggesting thatwork and marriagehave age-specific effectsrather than uniform effects on crime.Whether work is a turning point for offend-ers may also depend on other dimensions oftime, such as the duration since release fromprison. This investigation takes up threequestions about causality, age-dependence,and the timing of criminal recidivism. Doeswork cause a reduction in crime, or is theassociation spurious? Do work effects de-pend on the life-course stage of offenders,or are they uniform across age groups?When are released offenders at greatestriskof recidivism?WORK, CRIME, AND CAUSALITYWork is importantfor theories of crime be-cause workers are likely to experience closeand frequentcontact with conventional oth-ers (Warr1998) and because the informal so-cial controls of the workplace encourageconformity (Sampson and Laub 1993). Inparticular, life-course theories suggest that

Direct all correspondence to the author at De-partment of Sociology, University of Minnesota,267 19th Avenue S., Minneapolis, MN 55455([email protected]). Versions of thispaper were presented at the annual meetings ofthe American Sociological Association in NewYork in 1996 and the 12th InternationalCongressof Criminology in Seoul in 1998. I thank RossMatsueda, Irv Piliavin, Larry Wu, JeylanMortimer, Karen Heimer, Brad Wright, HiroshiTsutomi, Bob Mould, Brian Martinson,J. Dixon,Melissa Thompson, Janna Cheney, and the ASREditors and anonymous reviewers for advice andhelpful comments.

AMERICAN SOCIOLOGICAL REVIEW, 2000, VOL. 67 (AUGUST:529-546) 529


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530 AMERICAN SOCIOLOGICAL REVIEWemployment is critical to explaining desis-tance or cessation from crime (Shover1996). Because employment is so clearlymanipulable, work effects are amenable toboth scientific study and policy analysis. Infact, the provision of employment is one offew policy instrumentsat the state's disposal(Wilson 1975:59). It is much simpler toplace parolees into varying work statuses,for example, than to randomly assign themto different spouses, neighborhoods,or peerassociates.For all of these reasons, studies of workeffects on crime are prominentin the socio-logical literature(Berk, Lenihan, and Rossi1980; Crutchfield and Pitchford 1997;Hagan 1991; Sampson and Laub 1990;Thornberryand Christenson 1984). This re-search has established several basic empiri-cal findings. First, the meaning of work andits implications for crime appear to changeat some point during the transition to adult-hood: The bivariate association betweenemployment and law violation is generallypositive for juveniles (Bachman and Schu-lenberg 1993; Gottfredson and Hirschi1990:138; Mihalic and Elliott 1997; Ploeger1997; Wright, Cullen, and Williams 1997),but negative for adults (Farrington et al.1986; Hagan and McCarthy 1997; Sampsonand Laub 1990). Second, a high incidenceof shortjobless spells, ratherthan long-termunemployment, characterizes the work his-tories of criminal offenders (Cook 1975;Sullivan 1989). Although their earnings andemployment levels are far below those ofthe general population, most inmates haveat some point penetrated the paid laborforce (U.S. Department of Justice 1993).Given this instability, researchers must ad-dress both changes in work statuses overtime and the temporal sequencing of workand crime. Third, despite solid observa-tional evidence showing strong effects ofwork on adult offending (Sampson andLaub 1990), most experimental efforts toreduce crime throughemployment have hadnull or disappointingly small treatment ef-fects (Piliavin and Gartner 1981; Shermanet al. 1998). Although crime reductionamong older offenders has sometimes beenobserved in financial aid programs (Leni-han 1977) or employment projects (Gartnerand Piliavin 1988), adolescents generally

derive far smaller benefits from these pro-grams (Orr et al. 1996).Taken together, existing theory and re-search point to a complex and perhaps con-ditional relation between work and criminalbehavior. Whether this relation is causal re-mains unresolved, as does the direction ofcausality. Employmenthas been conceptual-ized as a cause of crime and conformity(Hagan 1993), a spurious correlate (Gott-fredson and Hirschi 1990:139), and as aproxy indicator of a latent causal factor(Sampson and Laub 1990). These differinginterpretations are partly due to the coinci-dence of employment with age and differinginterpretations of the relationship betweenage andcrime.AGE-INVARIANT ANDLIFE-COURSE CONCEPTIONSOF CRIMINAL BEHAVIORFor most offenses in most societies, crimerates rise in the early teen years, peak dur-ing the mid- to late teens, and decline there-after (Hirschi and Gottfredson 1983). Al-though the general contours of this "age-crime curve" are well established, thecurve's interpretation has been hotly de-bated. Those in the life-course camp arguethat the causes of crime are age-gradedandvariable over the life cycle (Greenberg 1985;Matza 1964; Sampson and Laub 1993;Shover 1996; Steffensmeier et al. 1989).From this perspective, the transition to em-ployment partially explains the relationshipbetween age and crime (Greenberg 1977;Grogger 1998; Pezzin 1995), and work ef-fects will vary with the age of the worker.Shover (1996), for example, suggests thatemployment reinforces an emergent non-criminal identity among older offenders butnot among younger offenders. These viewsimply that work is causally related to crimeand thatage interacts with workin its effectson crime.

In response to life-course arguments,Hirschi and Gottfredson (1983) argue thatage affects crime directly and "does not in-teract with any known causal variables in itseffect on crime"(p. 580). This "non-interac-tion hypothesis" (Tittle and Grasmick 1998:337) posits that age does not interact withwork or anything else in affecting crime.


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AGE, WORK, AND CRIME 531Withregardto employment,Gottfredson andHirschi (1990) maintain that "employmentdoes not explain, or help to explain, the re-duction in crime with age" (p. 139). Fromthis perspective, the association betweenwork andcrime is spurious due to a commoncause, such as impulsiveness or opportunity.In contrast to life-course approachesthat im-ply causality and interaction in the relationbetween age, work, and crime, GottfredsonandHirschi's argument mplies spuriousnessand noninteraction:Work is not causally re-lated to crime and age does not interact withwork or other variables.This debate has important implicationsfor science and policy. The age-invarianceargument suggests a focus on primary pre-vention (Gottfredson and Hirschi 1990:269). By allocating resources to younger"at-risk"populations, society reaps the mul-tiplier effects of a lifetime of industriousconformity. The life-course argument, incontrast, suggests age-graded correctionalprograms to reduce the social harm associ-ated with recidivism. From the latter per-spective, employment programs may be animportant turning point in the criminal tra-jectories of older offenders.The empirical standing of this debate isinconclusive. Life-course studies thatreporta negative correlation between work andcrime (Sampson and Laub 1990) generallyhave strings attached-the association be-tween work and crime is conditional onsome characteristic of the jobs or the work-ers. In their age-graded theory of informalsocial control, Sampson and Laub (1990:611) argue that it is not "employment perse," or "employment by itself' (Laub andSampson 1993:304) that reduces crime, butrather the stability and commitment associ-ated with work. Therefore, Sampson andLaub (1990) include measures such as workhabits in theirjob stability index.Those in the invariance camp, however,find such evidence unconvincing.If employ-ment effects are conditional on good workhabits, the putative "job effects" are taintedby "person effects" or preexisting workercharacteristics. Presumably, people withgood work habits would be less likely tocommit crime in the first place than peoplewith poor work habits, even in the absenceof employment. To arbitrate between these

positions, some mechanism to sort "job ef-fects" from "personeffects" is needed. Twosuch mechanisms are random assignment(Campbell andStanley 1963; Cox 1958) andstatistical corrections for sample selectivity(Winshipand Mare 1992).The relative superiority of experimentalover nonexperimentalmethods is a matter ofsome debate in the programevaluation lit-erature (Fraker and Maynard 1987; Heck-man 1992; Heckman and Hotz 1989; La-londe 1986). Unlike nonexperimentalmeth-ods, however, a properly conducted experi-ment does not reduce the precision of esti-mates (Manski 1995) or require additionalstatistical assumptions (Heckman and Hotz1989:862). Experiments on the effect of em-ployment on crime convert uncontrolledvariation in personal criminal propensitiesinto random variation, allowing researchersto make probabilistic statements aboutwhetherjob effects are due to chance (Rubin1974). Therefore, designs that randomly as-sign people to work and nonwork statusesare preferred over nonexperimentaldesigns,especially when a convincing model of se-lection into employment is unavailable(WinshipandMare 1992).WORK AND THE DURATION STRUCTUREOFRECIDIVISMExperimental studies of employment pro-grams for offenders reportthat work effectsmay dissipate over time (Cave et al. 1993;Mallar et al. 1982). Moreover, the effect ofemployment is likely to be strongest whenrecidivism is most likely (Ekland-OlsonandKelly 1993; Schmidt and Witte 1988). Forthese reasons, the measurement of worktreatmenteffects is another mportantdesignspecification. To determine when and howemployment affects recidivism, I use workeffects that correspondto three distinct be-havioral models. Assignment, participation,and eligibility specifications are distin-guished by duration(short-termversus long-term) and whether they are conditional onparticipationin a work program.

ASSIGNMENT EFFECTS. These effects arestandard ndicatorsof programeffectiveness,determinedby assignmentto the work treat-ment or control condition. Assignment ef-fects correspond to a turning point model:


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532 AMERICAN SOCIOLOGICAL REVIEWDid the work experience permanently altertrajectories of offending? This is the mostconservative test of work effects because itencompasses the overall impact of being inthe program, long term and short term, re-gardless of the level of participation.

PARTICIPATION EFFECTS. In contrast toassignment effects, participation effects re-fer to the immediate contemporaneous im-pact of actuallyworkingin the job. This cor-responds to an involvement or time alloca-tion model of work and recidivism (Hirschi1969:187). To use a metaphorfrom the drugand alcohol literature: Are respondents onthejob when they fall off the wagon?ELIGIBILITY FFECTS. These effects fallsomewhere between assignment andpartici-pation effects. They are limited-term mea-sures of treatment impact during periodswhen those assigned to the program couldhave worked in program obs. Eligibility ef-fects correspondto ajob opportunitymodel:Having a job available may provide a tem-porary safety net that reduces recidivismduringthe eligibility period,but workoppor-

tunity will not have lasting effects after theprogramends.

DATA: THE NATIONALSUPPORTED WORKDEMONSTRATION PROJECTDESIGN OF THE EXPERIMENTTo test these job-treatmenteffects, I analyzedata collected in a large-scale experimentalemployment program, the National Sup-ported Work Demonstration Project. Theprogram drew participantsfrom the ranks ofthe underclass or ghetto poor. People werereferred to the programby criminaljustice,social service, andjob-trainingagencies andrandomly assigned to experimentalandcon-trol conditions. FromMarch 1975 until July1977, over 3,000 persons with an official ar-resthistorydrawnfromnine U.S. cities wererandomly assigned to the control or treat-ment condition and completed initial (base-line) interviews. Those in the treatmentgroup were offered minimum-wage jobs(mainly in the construction and service in-dustries) in crews of 8 to 10 workers led bya counselor/supervisor. Members of bothgroups reportedwork, crime, and arrestin-

formation at nine-month intervals for up tothree years.1PROFILE OF THE PARTICIPANTSThe supportedwork project targeted crimi-nal offenders, hardcore drug users, andyouth dropouts with a history of bothchronic and recent unemployment. To jointhe program,offenders were requiredto havebeen incarceratedwithin the past six months,addicts were required to have attended adrug treatmentprogram, and at least half ofthe youth dropouts were required to have anofficial delinquent or criminal record.Screening for these conditions was con-ducted separately from the assignment pro-cess, so it would not compromise random-ization (although generalizations may belimited to extremely disadvantaged personswith official criminalhistories). Because thisstudy examines recidivism or reoffending,only those with an official arrest history (85percent of the sample) are included in theanalysis.2

Table 1 shows mean values on personalcharacteristics for members of the controland treatmentgroups. In both groups, mostsubjects were young African Americanmales with less than a high school educationand an extensive criminal history. Those inthe treatment group were slightly older,more likely to have a history of economiccrime, and less likely to be assigned to theyouth offender group.3Because respondents were randomly as-signed to the treatment (work) or control

'Of those scheduled for interviews, completionrates were 77 percent at nine months, 70 percentat 18 months, and 60 percent at 36 months. In-carceratedsubjects were interviewed in jail or inprison. A sample selectivity analysis found noevidence that program effects on arrest and otheroutcomes were biased by sample attrition(Brown1979; Piliavin and Gartner 1981).2 The results reported below also hold whenpersons who had no arrest history at the baselineinterview are added to the analysis.3 To equalize the age distributions in the treat-ment and control groups, I reestimated all mod-els after excluding members of the youth drop-out group. Analysis of this subsample yielded thesame substantive conclusions as those presentedin Tables 2 and 3.


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AGE, WORK, AND CRIME 533Table 1. Characteristics by ExperimentalGroup: Participants in the NationalSupported Work DemonstrationProject, 1975 to 1977

Experimental GroupPersonal Characteristics Control TreatmentAge (mean) 24.6 t 25.2(6.6) (6.6)Percent male 92.0 91.6Racial Group (Percent)

African-American 76.2 76.9White 11.1 11.7Latino 12.3 11.0

Percent married 13.7 14.1Years of education (mean) 10.2 10.3(1.8) (1.8)Longest job duration 15.2 17.1

(mean, in months) (21.1) (24.5)Criminal History

Percent with prior crime 74.8 t 79.2for moneyNumber of arrests (mean) 8.3 8.7

(11.4) (11.7)Project Site (Percent)

Jersey City 18.1 18.9Hartford 20.5 t 14.0Philadelphia 16.2 18.2Oakland 14.2 15.4Chicago 12.4 13.9Newark 10.0 10.7San Francisco 5.6 6.4New York 2.1 1.6Atlanta .7 .7

Sample Group (Percent)aOffender 52.6 53.8Addict offender 28.5 31.2Youth offender 18.9 t 14.9Notes: Numbers in parentheses are standard de-viations. Number of cases in the control group rangefrom 1,937 to 2,210; for the treatment group, the

number of cases ranges from 1,821 to 2,052.a The combined offender sample includes all sup-ported work participants who had been arrested atleast once prior to program entry. This includesmembers of the groups called "ex-offender,""former addict," and "youth dropout" in the initialevaluation (Hollister et al. 1984).t Difference between control and treatmentgroups is significant at p < .05 (two-tailed tests).

groups, program assignment is uncorrelatedwith work habits or other unmeasured"per-son effects" that might contaminatejob ef-fects. These data are therefore appropriatefor testing hypotheses such as the Gottfred-son-Hirschi age-invariance argument. Al-though supportedwork data have been care-fully examined in a number of articles(Matsuedaet al. 1992; Piliavin et al. 1986),the full continuous-time data arrayshave yetto be analyzed. In light of the $100 millionprogram cost (Hollister, Kemper, and May-nard 1984), the obstacles to replicating theNational Supported Work DemonstrationProject's experimental design, and the richemployment and crime information col-lected, these data are perhaps uniquelysuited to unraveling the relation betweenage, work, and criminalrecidivism.

SPECIFIC INDICATORS ANDANALYTIC STRATEGYMEASURING ASSIGNMENT, ELIGIBILITY,AND PARTICIPATIONAssignmentis measuredby a fixed covariatecoded 1 for those assigned to treatmentand0 for those assigned to the control group.The analysis of assignment effects is basedon the classic true experimental design(Campbell and Stanley 1963; Cox 1958). Ifthere had been no systematic departuresfrom randomization, no attrition, and notime dependency in work effects, I couldlimit the analysis to assignment effects. Be-cause these conditions were not met in thesupportedwork project, I adjustassignmenteffects for several covariates, and analyzeeligibility andparticipationas well.Eligibility is measured as a time-varyingdichotomous indicator for those assigned tothe experimental condition. Those assignedto the treatmentgroup were eligible for ei-ther 12 or 18 months of employment andwere coded 1 duringthese months.4 For fol-low-up interviews beyond their eligibilityperiod, however, those assigned to the treat-ment groupwere ineligible and coded 0. All

4 Thoseassigned n theNewark,Philadelphia,andHartfordites wereeligible for 18 months;those in the other sites were eligible for 12months.


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534 AMERICAN SOCIOLOGICAL REVIEWmembers of the control group, of course,were ineligible for all periods and werecoded 0. Because the length of eligibilitywas predetermined by the research design,estimatedeligibility effects are also based ona true experimental design.Participation is modeled as a time-depen-dent explanatory variable, coded 1 duringperiods of work in a program ob and 0 oth-erwise. Random assignment ensures that thecontrol and the treatmentgroups have equalproportionsof crime-prone ndividualsat thestartof the experiment.Over time, however,criminal propensity (or any other correlateof crime) may be associated with project at-trition. If crime-prone individuals are morelikely to abandon treatment obs, the pool of"jobleavers"will be increasingly composedof more crime-pronepeople, and the pool of"job stayers" will be increasingly composedof less crime-prone people. Because theseselection processes may bias participationeffects on crime, I include in the participa-tion models a time-varying term for havingleft supported work. And, because at leastsome of the "leavers"left for better oppor-tunities outside the program, I also adjustprogrameffects with time-varyingterms forregular full-time employment and schoolparticipation.Although these statistical ad-justments do not addressselectivity as effec-tively as randomization would, the partici-pation models illustrate the patternof con-temporaneousassociation (if not causation)between work, crime, and arrest.AGE AND OTHER INDEPENDENTVARIABLESI test for differences in survivor functionsbetween the work treatment and controlgroups, stratifying by age of participants.After examining a number of different ageclassifications, I chose age 26 as a discretecutting point for both empiricaland theoreti-cal reasons. First, the strongesteffects on ar-rest in an influential financial aid experimentwere found for those aged 26 and older(Mallar and Thornton 1978). Second, thoseolder than 26 are specifically excluded fromseveral large-scaleprograms,such as the JobCorps (Lattimore, Witte, and Baker 1990;Mallaret al. 1982). Third,the median age ofU.S. prison inmates is approximately 26

(U.S. Department of Justice 1993). Fourth,labor force participation rates for men andsingle women peak between ages 25 and 34(U.S. Census Bureau 1998:408). Finally, us-ing an age threshold of 26 assures a sampleof almost 1,000 offenders in each age cat-egory.Apart from these empirical grounds for anage-26 cutting point, there are also theoreti-cal reasons to select an age threshold thatexceeds the age 18 to 22 range that charac-terizes the transitionto adulthood n the gen-eral population. In view of their criminalhistories and spotty employment records,standard ransition-agenorms areunlikely toapply to criminal offenders (Hogan andAstone 1986). Rather, theories of criminalembeddedness suggest that juvenile crimeisolates adolescents from education andwork networks that help initiate and sustainadultemployment (Hagan 1993). By the ageof 26, when crime rates have begun to de-cline, transitions to marriage and employ-ment may facilitate cessation or desistancefrom crime.I estimate models that include age (as botha discrete and continuousvariable)and workeffects. In addition, I also present modelsthatadjustthese effects for otherexplanatoryvariables. Covariance adjustment improvesthe precision of estimators and helps over-come the effects of incomplete randomiza-tion or selective attrition. Criminal history,measuredby the numberof arrestsand a di-chotomous indicator of prior economiccrime, is included in the adjusted models.Controls for years of education, longest jobduration, race, sex, marital status, andsample group are also entered in adjustedmodels, as well as programcohort (becausestronger work effects were found amongearly enrollees [Hollister et al. 1984:39]),andthe project site unemploymentrate.DEPENDENT VARIABLESI analyze the timing of the first self-reportedarrestafterprogram entry.The first arrestisespecially salient to released offenders, of-ten representinga parole violation with seri-ous consequences. Because theories of crimepredict strong relationships between eco-nomic crimes and employment (Merton1938), I also examine the timing of the first


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AGE, WORK, AND CRIME 535spell of illegal earnings.5 This indicator isbased on a semi-monthly self-reported arrayof crimes committed for money (e.g., bur-glary, robbery, drug sales). Comparing theresults for arrest and illegal earnings helpsdisentangle the behavior of offenders fromthe actions of the criminal justice system.Police discrimination against unemployedsuspects, for example, would inflate esti-mates of work effects on arrest but wouldnot bias work effects on illegal earnings.To evaluate the validity and reliability ofthe supportedwork data, Schore, Maynard,and Piliavin (1979) comparedofficial arrestdata with self-reported arrestdata for threeof the programsites. They found a 45 per-cent underreportingof arrestincidence, butonly a 20 percent underreportingof arrestprevalence.Thus, people were more likely tounderstate he numberof their arreststhan toerr in reporting whether they had been ar-rested at all. Nevertheless, Schore et al.(1979) found that experimental status wasunrelated o underreporting, ndtherewas noevidence that those assigned to jobsunderreportedn an effort to keep them. Thefollow-up observationperiodrangedfrom 18to 36 months, depending on the date of en-rollment into the program.All subjects werescheduled for an 18-monthinterview; at thispoint, 38 percent had been arrested and 32percentreported llegal earnings.After threeyears, when all follow-up interviews werecompleted, approximately54 percent of therespondentshad been arrestedand 38 percenthad reportedearningmoney illegally.METHOD: EVENT HISTORYANALYSISEvent history analysis is suited to the studyof work and recidivism. Relative to cross-sectional or panel designs, event history ap-proaches (1) increase the precision of esti-mated work effects, (2) aid in determiningthe sequencing of work and crime, (3) ap-propriatelymodel censored cases (i.e., those

5 For both theoretical and practical reasons, Ilimit the analysis to the timing of the first arrestandthe first period of illegal earnings.These firsttransitions are particularly critical for releasedoffenders and are likely to differ qualitativelyfrom subsequent transitions.

who never reoffend) over varying observa-tion periods, and (4) allow for time-varyingas well as fixed work effects. I estimate pro-portional and nonproportional piecewisemodels that specify an exponential baselinehazard (Tuma, Hannan, and Groeneveld1979; Wu and Tuma 1991:362). In thesespecifications, the dependentvariable is thenatural logarithm of the hazard of arrest orillegal earnings, defined as an instantaneousprobability.Most recidivism studies operate on a "du-ration clock," modeling the time fromprisonrelease until parole violation, rearrest,or re-conviction (Ekland-Olson and Kelly 1993;Hepburn and Albonetti 1994; Maltz 1984;Schmidt and Witte 1988). In most experi-ments, however, the analysis begins at thetime of randomization,ensuringthat the dis-tribution of other time origins is approxi-mately equal across the treatmentand con-trol groups. I estimated models on bothclocks, but present results based on the ex-perimentalclock because my primaryinter-est is the work treatment effects.6 To assurethe temporal ordering of work and crime, Ilag participation ndicators by two weeks (sothat employment at time t predicts crime attime t + 2 weeks).

RESULTSA NONPARAMETRIC ANALYSIS OFASSIGNMENT EFFECTSI use standarddemographiclife table meth-ods (Namboodiri and Suchindran 1987) toexamine the time until the first arrest andthe first period of illegal earnings after thestart of the experiment. These approachesare nonparametric in the sense that theymake no distributional assumptions aboutthe underlyingrecidivism process. To deter-mine whether supportedemployment affectsrecidivism rates, the survival distributionsfor those assigned jobs are compared withthe correspondingdistributions for the con-trol group. In the figures that follow, thehorizontal axis represents time in monthsand the vertical axis represents the cumula-

6 The substantive conclusions of analyses runon the duration clock are identical to those dis-cussed for the experimental clock.


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536 AMERICAN SOCIOLOGICAL REVIEW(a) Offenders Ages 26 and Under1.0 -

a' -~~~~~~~'\~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~reatmentgroup-----Control group0

00.0 .6

N =2,125; logrankX2 .2 (d.f.=1).30 6 12 18 24 30 36

Duration in Months

(b) Offenders Ages 27 and Over

- Treatmentgroup----Control group

0

0o .6 *

E04

N = 980; log rank%2 10.7 (p < .001, d.f.= 1

.3

0 6 12 18 24 30 36Duration n MonthsFigure 1. Time to Arrest for Younger Offenders and Older Offenders: National Supported WorkDemonstration Projecttive proportion of those at risk of arrestwhohave yet to report an arrest.Both groups be-gin the experiment at "1" on the verticalaxis because all respondents are at risk andnone has yet failed by being arrested.I as-sess the statistical significance of the treat-

ment effect with a log-rank test of survival-curve equality.Figure la compares those in the experi-mental and control groups aged 26 and un-der; it depicts an ineffective treatment. Thesurvival distributions are virtually indistin-


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AGE, WORK, AND CRIME 537

? .9- ------------------------- -------- - - -- - ----- Treatm nt group-Control group

-E 60

N = 1,033;log rank%2 = 6.3 (p < .01, d.f.= 1).5 0 6 12 18 24 30 36

Durationin MonthsFigure 2. Time to Illegal Earnings for Offenders Ages 27 and Over: National Supported WorkDemonstration Project

guishable for the first year, when 69 percentof those in both the treatment and controlgroups have yet to be arrested. After twoyears, about 55 percent of those in the con-trol group remain free from arrest comparedto only 54 percent of the treatment group.Although this gap widens slightly through-out the observation period, the differencebetween the distributions is neither statisti-cally nor practically significant. Figure lbdepicts the curves for those aged 27 andolder and paints a much different picture ofprogram effectiveness. Here the line indi-cating the survival function for the treat-ment group diverges from the correspond-ing line for the control group after approxi-mately six months. The difference graduallyincreases from 8 percentage points at oneyear to 11 percentage points after 3 years.As the chi-square statistic shows, this gapconstitutes a strong and statistically signifi-cant treatmenteffect for employment in thisolder group.Gottfredson and Hirschi (1990) argue thatminor or trivial variations do not disprovethe invariance thesis and that the empiricalrelation between employment and crime is"too small to be of theoretical import" (p.164). What, then, is the practical signifi-cance of these statistical tests? The curvesplot the estimated probability of arrestamong offenders who have not been arrestedby time t. If recidivism is defined as theprobabilityof arrest after 18 months, Figure

lb shows 30 percent recidivism in the ex-perimental group compared with about 40percent in the control group. This 10 per-centage-point difference corresponds to a24-percent reductionin recidivism when thecontrol group is taken as the base rate([.300 -.397]/.397 = .24). In light of thewidely reported failure of correctional treat-ment (Martinson 1974) and the high costs ofthe criminal ustice system, a reductionin re-cidivism of this magnitudeis noteworthy.As was true for arrests, supported workassignment fails to reduce the rate of illegalearnings among the total sample or amongthe younger subgroup (figures not shown).For those aged 27 and over, by contrast, Fig-ure2 shows thatthe workprogramdecreasesrecidivism among those in the experimentalgroup relative to the control group. Amongolder offenders, the cumulative proportionreporting illegal earningsis about 7 percent-age points lower for those assigned to theprogram than for those in the control group,a statistically significant treatmenteffect.A COMPARISON OF THE DURATIONSTRUCTURES OF ARREST AND ILLEGALEARNINGSComparison of Figure lb and Figure 2 sug-gests differences in the durationstructuresofarrest and illegal earnings, as well as a moregradual work effect on arrest than on illegalearnings. Studies of official recidivism gen-


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538 AMERICAN SOCIOLOGICAL REVIEW.040 .

16- Treatmentgroup~ .035 ----- Controlgroup? 030---- --------- ------------------------------------ -----------------------

E 025

Wn .020-.0

r .015-.

act .0120.-? 015 ---------------------------------- - -- ------------- ------

U.N

.0 0 I I_=.005 ---------H

3 9 15 21 27 33Duration in MonthsFigure 3. Time to Arrest for Offenders from All Age Groups: National Supported Work

Demonstration Project

E .018- -------------------------------------- ----- Treatm e nt group ------.016 -------- -------------------- --------------- ---C o ntro group ------

'a .014.012----------

wU .008. .006 - - - - - - - - - - - - - - - - - - - - - - - -

.002= 0 ~~~~~39 15 21 27 33

Duration n MonthsFigure 4. Time to Illegal Earnings for Offenders from All Age Groups: National Supported WorkDemonstration Projecterally reportrising hazardrates for the firstyear after release and declining rates there-after (Chung, Schmidt, and Witte 1991;Ekland-Olson and Kelly 1993). Is this log-normal pattern due to an initial abstinenceperiod or to lags in criminal justice process-ing? This puzzle is at least partiallyresolvedby plotting the hazard functions of the tworecidivism measures. Figure 3 shows the fa-miliar rising and falling pattern observedwhen arrest or conviction is used to measurerecidivism. The rate of first rearrest rises

during the first nine months and begins anuneven decline thereafter.In contrast to the arrest hazard, however,the hazard for illegal earnings (Figure 4) de-creases monotonically, with the steepest de-cline occurring in the first year of the pro-gram. The greatest recidivism risk for ille-gal earnings therefore occurs immediatelyafter release. To test whether this result isdue to running the analysis on the experi-mental time clock rather than the duration-since-release clock, I limited the sample to


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AGE, WORK, AND CRIME 539recent prison releasees, starting the clock forboth arrest and illegal earnings outcomes atthe first month of release from prison. Thediscrepancies between the two recidivismoutcomes for this subgroup are even morepronounced than those shown in Figures 3and4 (availableon request from the author).One interpretationof these results is thatthe rising arrest hazard reflects a delayedcriminaljustice response rather than an ini-tial period of abstinence. For example, pa-role or police officers may not react to anoffender's illegal activities until they be-come sustained or repeated. Alternatively,the two measures may be tapping differentdomains of activity, as Hindelang, Hirschi,and Weis (1979) have arguedwith regard toself-reported and official delinquency mea-sures. Among supported work participants,the self-reported illegal earnings indicatormay represent less serious offenses that areinvisible to the criminaljustice system. Forexample, whereas selling a small amount ofmarijuanato a close friend may generate il-legal earnings, selling drugs on a street cor-ner is more likely to result in arrest. Thusself-reportsfrom adult offenders may sufferfrom the same domain limitations as do in-dicators of adolescent delinquency.Whether the discrepancies in the hazardrates are due to delays in criminaljustice re-sponses or to domain differences, however,there is no discrepancy in the age-gradedwork effects: The survival curves show thatsupported employment decreases the likeli-hood of recidivism for both measures amongolder offenders. Although this finding is notincontrovertible, it is based on a solid ex-perimental design and an analytic techniquethat makes few assumptions.PROPORTIONAL MODELS OFASSIGNMENT, ELIGIBILITY,AND PARTICIPATIONTables 2 and 3 elaborate the nonparametricresults, showing the main and interactive ef-fects of age and work on arrest and illegalearnings, alongside estimates from modelsthat adjust these effects for race, sex, mari-tal status, program cohort, work history,prior economic crime, prior arrests, samplegroup, and site unemploymentrate. Resultsfrom the adjustedmodels test the robustness

of the experimental work effects, thoughthey do not supplant them (Zeisel 1982).ASSIGNMENT EFFECTS. Model 1 ofTables 2 and 3 shows the main effects of as-signment to treatment and of age on arrest(in Table 2) and illegal earnings (in Table 3).For arrest Table 2), the treatmentassignmenteffect is close to zero (-.03), andthe signifi-cant age coefficient (-.12) suggests that olderoffenders are less likely thanyoungeroffend-ers to be arrested.The joint assignmentspeci-fication for Model 1 adds an age x treatmentassignment interaction term, reflecting thepredictions of the life-course model. Thisequation provides a much better fit to the

data. Under this joint model, the assignmentcoefficient represents the effect of assign-ment to treatment among younger partici-pants and the age coefficient represents theage effect within the control group. Both arenonsignificant and positive. The estimate forthe age x treatment assignment interactiongives the difference in the treatmenteffectacross age groups, providing a direct test ofthe age-invariance hypothesis. The signifi-cant negative interaction term for arrest inTable 2 (-.31) suggests a differential ratherthan an invariant reatmenteffect by age. Thetreatmenteffect on arrestfor the older groupis obtained by deviating the interactioncoef-ficient from the assignment coefficient: Therelative risk of arrest is about 22 percentlower for older assignees than for older con-trols (1 - e(-.31l+06) .22).7 After covariate ad-justment, the interaction term is slightly re-duced but remains statistically significant.

In the analysis for illegal earnings shownin Table 3, the work treatment effect againdiffers across age groups, as reflected in thesignificant interaction terms of thejoint andadjusted models. This finding holds net ofpriorcriminal andemployment history,mar-ital status, and other background character-istics. Results for both arrest and illegalearningsthus support he conceptionof workas a turningpoint in the life course for older,but not younger, offenders.ELIGIBILITY EFFECTS. Eligibility refersto the opportunity to participate in sup-

7 To testwhether lder reatmentssigneesdif-fer fromoldercontrols, modified odingschemeis required. n this case, the differencebetweenthetwogroupss statistically ignificant.


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540 AMERICAN SOCIOLOGICAL REVIEWTable 2. Maximum-Likelihood Estimates of the Effect of Work on Self-Reported Arrest: Offendersfrom the National Supported Work Demonstration Project

Model 1 Model 2 Model 3Independent (Treatment Assignment) (Eligibilitya) (Participationa)Variable Main Joint Adjusted Main Joint Adjusted Main Joint AdjustedAge > 26 -.12* .03 .08 -.12* -.01 .05 -.13* -.08 -.02(.06) (.08) (.09) (.06) (.07) (.08) (.06) (.07) (.07)Treatment -.03 .06 .05assignment (.06) (.07) (.07)Age x treatment -.31** -.28*assignment (.12) (.12)Eligibilitya -.07 -.02 .01(.06) (.07) (.07)Age x eligibility - -.32* -.30*

(.13) (.13)Participationa -.30*** -.22* -.23*(.08) (.09) (.09)Age x participation -.28 -.26

(.16) (.16)Left program - - .1111 .1* .10(.06) (.06) (.06)Regular worka -.29*** -.29*** -.31 ***

(.07) (.07) (.08)In school -.25 -.25 .24

(.14) (.14) (.14)Chi-square 198 204** 319*** 199 205** 321*** 234** 237*** 352***Degrees of freedom 3 4 16 3 4 16 6 7 19

Note: Piecewise proportional models; N = 3,102. Numbers in parentheses are standard errors. Overallsignificance tests are based on improvement in fit over baseline model with no covariates.aTime-varying covariate.


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AGE, WORK, AND CRIME 541Table 3. Maximum-Likelihood Estimates of the Effect of Work on Self-Reported Illegal Earnings:Offenders from the National Supported Work Demonstration Project

Model 1 Model 2 Model 3Independent (Treatment Assignment) (Eligibilitya) (Participationa)Variable Main Joint Adjusted Main Joint Adjusted Main Joint AdjustedAge > 26 -.11 .07 -.01 -.11 .03 .01 -.11 .01 -.07(.07) (.09) (.10) (.07) (.09) (.08) (.07) (.08) (.09)Treatment -.02 .09 .03 - - -assignment (.06) (.07) (.07)Agex treatment -.36** -.31*assignment (.13) (.13)Eligibility -.03 .06 -.01(.07) (.07) (.08)Age x eligibility -.31* -.28*

(.13) (.14)Participationa -.06 .06 -.003(.07) (.08) (.08)Age x participation -.39** -.36*

(.15) (.15)Left program .06 .06 .01

(.09) (.09) (.09)Regular worka -.08 -.08 -.11

(.09) (.09) (.09)In school -.01 -.01 -.03(.13) (.15) (.14)Chi-square 1,246 1,253* 1,495*** 1,246 1,252* 1,494*** 1,247 1,255 1,498***Degrees of freedom 4 5 17 4 5 17 7 8 20

Note: Piecewise proportional models; N = 3,300. Numbers in parentheses are standard errors. Overallsignificance tests are based on improvement in fit over baseline model with no covariates.a Time-varying covariate.*p < .05 **p< .01 < .001 (two-tailed tests)

der the eligibility or assignment specifica-tions-are likely to avoid arrest during peri-ods when they are actively working in theprogram.Although the difference in partici-pation effects (representedin the interactionterm) is not statistically significant in Model3, participation reduces the rate of arrest forthe elder group by approximately 39 percentin both the joint and adjusted models. Con-sistent with the involvement model of par-ticipation, leaving the program has a mar-ginal positive effect on arrest and securingregularemployment a strong negative effecton arrest, net of the othervariables.In contrast to the arrestresults, the resultsfor illegal earnings show an age-graded par-ticipationeffect with a significant age x par-ticipation interaction in the joint models(Table 3, Model 3). The weak participation

coefficients for the younger group in thejoint and adjusted models mean that sup-ported employment fails to reduce the rateof illegal earnings for younger offenders,even during periods of active program in-volvement. Also in contrast to the arrestmodels, regular employment and school at-tendance have little effect on the hazard ofself-reported illegal earnings.Tables 2 and 3 show that the effects ofwork on crime depend on the specificationof the independentand dependentvariables.For the arrest outcome, the program s effec-tive for older offenders under all specifica-tions and for both younger and older offend-ers under a model specifying contemporane-ous participation. For the illegal earningsoutcome, supportedworkreduces recidivismfor older but not for younger offenders


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542 AMERICAN SOCIOLOGICAL REVIEWacross the treatmentassignment, eligibility,and participationmodels.ALTERNATIVE SPECIFICATIONSAGE. Age and each of the age x work inter-action termscan also be modeled as continu-ous variables, corresponding to the view thatoffenders are increasingly amenable to em-ployment with each passing year. Under thiscoding, the interaction is again negative andstatistically significant, but the main experi-mental effect is positive and marginally sig-nificant (not shown). I present the categori-cal results in this article because plots of age,programstatus, and recidivism suggest a dis-crete rather than a continuous interaction.Moreover, the threshold is easier to interpretand moreapplicablefor policy purposes.Themodels in Tables 2 and 3 assume that agematters most when the opportunityto workis initially offered-that amenability to treat-ment is fixed at programentry.Age may alsobe specified as time-dependent, assumingthatpeople "ageinto amenability"duringtheexperiment. Under this specification, thesubstantive results are almost identical tothose reportedin Tables 2 and 3.

NONPROPORTIONAL PARTICIPATIONMODELS. The survival and hazard plotsshow that rearrest rates peak later than docrime rates and that treatment effects arestrongestat the beginning of the experiment.I therefore reestimated the models in Tables2 and 3 using nonproportional piecewisehazard models (not shown). For the arrestoutcome, results for the nonproportionalmodels parallel those for the proportionalmodels, with workparticipationreducingar-rest in both the main and joint models ineach period. For illegal earnings, however,the beneficial treatment effects and age xtreatmentassignment interaction effects dis-sipate rapidly after the first year. In light ofthese results, and the small differences be-tween the treatmentassignment andeligibil-ity models, it is unlikely that extending thesupported work eligibility period wouldhave had a markedeffect on recidivism.DISCUSSION AND CONCLUSIONThis article has examined whetheranexperi-ment that provided jobs to criminals served

as a turning point in their offending careers.In light of the rising number of released of-fenders rejoining civil society, it is importantto identify such turning points for both sci-entific and policy purposes. This year, over500,000 criminals will exit from state andfederal prisons (U.S. Department of Justice1999), and approximately 2 million will bereleased from probation or parole supervi-sion. Given the high likelihood of recidivismamong this group, it is good policy to seekthe turning points that enhance desistancefrom crime and facilitate rehabilitation.Moreover, the identification of such contin-gencies is fundamentalto scientific explana-tions of criminal behavior over the life cycle.Work appears to be a turning point in thelife course of criminal offenders over 26years old. Offenders who are provided evenmarginal employment opportunitiesare lesslikely to reoffend than those not providedsuch opportunities. Employment in the Na-tional Supported Work DemonstrationProject-a program critics deemed to be afailure-significantly reduced recidivismamong offenders over the age of 26. Primaryfindings reportedhere indicate: (1) a differ-ential work effect across age groups, lend-ing support to a life-course rather than anage-invariantmodel of work and crime; (2)progressively larger effects of assignment,eligibility, andparticipation, particularlyforthe arrestoutcome; and (3) the varying tim-ing of recidivism as measured by self-re-ported illegal earningsand arrest.I first examined the effect of programas-signment within age categories using testsfor the equality of survival distributions.Al-though the programfailed to reduce crimeacross the entire sample, its impact wasclearly age-graded: The job treatment sig-nificantly reduced recidivism among olderparticipants.In contrast to the stylized cul-turalimage of the "hardened riminal,"theseresults suggest that older offenders are moreamenable to employment interventions thanyounger offenders. Because these findingsare based on a sound experimental designand on an analytic technique that makes fewassumptions, the evidence is strong againstthe noninteraction hypothesis proposed byHirschi and Gottfredson(1983). The sugges-tion that older offenders generally desistfrom criminal behavior is unremarkable.


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AGE, WORK, AND CRIME 543These results are important because theyshow that older offenders given jobs are lesslikely to reoffend than those of comparableage who were not provided these opportuni-ties. This age x treatmentassignment inter-action suggests that workhastens desistancefrom crime among older offenders.The success of work programsin promot-ing desistance from crime for older offend-ers and their failure to preventcrime amongadolescents indicate asymmetric or irrevers-ible causation: The conditions that stopcrime in adulthood are not simply the re-verse of those that caused it in adolescence(Uggen andPiliavin 1998). Whereasparents,peers, and neighborhoods are inarguablyamong the initial causes of crime, for ex-ample, work and family factors take prece-dence in explaining desistance. The samegeneral theoretical models of age-gradedin-formal social control (Sampson and Laub1993), peer association (Warr 1998), orcriminal embeddedness (Hagan and Mc-Carthy 1997) may well apply to both pro-cesses. Nevertheless, the transition out ofcrime is distinguished by its clear link toadult life-course progressions andtransition-age norms. In fact, desistance from minordelinquency and crime may itself constitutea separate and important dimension of themultifaceted transition to adulthood.A second contributionof this study is theidentification of a range of work treatmenteffects on crime. I report treatmentassign-ment, participation, and eligibility effectsboth before and after adjustingfor personalcharacteristics related to both work andcrime (i.e., sex, race, maritalstatus, programcohort, education, work history, prior crimeand arrests, and site unemployment rate).The substantive results suggest that maxi-mizing participation rates-keeping thoseassigned to the experiment from droppingout of work-may increase program effec-tiveness, notwithstanding the "person ef-fects" driving both participationand recidi-vism. As cautioned above, however, partici-pation effects may be biased upward by se-lective attrition. On the other hand, assign-ment effects may be biased downward be-cause the observation period exceeds thelength of the program. Modeling programeligibility as a time-varying characteristicresults in a more sensitive estimate of pro-

gram effects that retains the experimentaldesign and sidesteps some of the selectivityproblems inherent in participation models.For illegal earnings, results from all threemodels tell the same substantive story. Ineach case, the hazard of illegal earnings issignificantly lower for older members of thetreatment group than for older members ofthe control group. For arrest, the magnitudeand even the direction of work effects varieswith the specification: The assignment re-sults (Model 1) show an age x treatmentas-signment interaction, the eligibility models(Model 2) show a stronger age x eligibilityinteraction, and the participation specifica-tion (Model 3) shows a strong effect for bothyounger andolder participants.Although theassignment and eligibility results are com-parablein this study, the conceptual distinc-tion between a turning point model and atreatmentavailability model may be impor-tant in a variety of research settings. More-over, the similarity of results in this case isrelevant for policy purposes-extending theeligibility period would appearto have littleadditional effect on recidivism. Analysis ofeligibility effects may be informative inother settings in which the follow-up periodexceeds the durationof the programandpar-ticipationis voluntary,such as predictingthetime until employment in a welfare-to-workexperiment.My third main finding indicates differentduration structures for self-reported illegalearnings and self-reported arrest.Althoughthe risk of recidivism via illegal earnings ishighest immediately upon release fromprison, with no initial abstinence period, therisk of recidivism via arrest rises moregradually over the first post-release year.These differences could be due to lags incriminaljustice processing or to the two out-comes (arrest and illegal earnings) tappingdifferentdomains of behavior.In eithercase,however, this finding highlights the poten-tial importanceof self-reported offending in-formation in scientific research on recidi-vism and desistance. Although reliance onofficial statistics has been challengedin etio-logical studies of delinquency,most investi-gations of adult offenders accept such mea-sures uncritically.When criminals are askedto report on their crime and arrest records,they tell us they aremost likely to commit at


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544 AMERICAN SOCIOLOGICAL REVIEWleast some forms of economic crime imme-diately upon release, but are most likely tobe arrested some time thereafter.Althoughthe age-gradedjob effects I examined holdacross these two outcomes, recidivism stud-ies can only be strengthened by validatingarrest or conviction data with self-reportedinformation on offending.Because this analysis examines data fromthe American underclassin the late 1970s, itis unclear whether the results are generaliz-able to less marginalizedpopulations in dif-ferent labor markets. Nevertheless, thesefindings should encourage employment,training(SampsonandLaub 1996) and otherinterventions (Sherman et al. 1998) for seri-ous adult offenders. These results also go tothe heart of a much largerdebate within thesociology of crime: Are crimes discreteevents in the life course, or is criminality astable characteristic of persons themselves?The latterview, that crime results from indi-vidual personality traits, implies that crimi-nal propensity crystallizes sometime duringsocialization in early childhood and is virtu-ally impossible to change thereafter.In con-trast, this study provides clear evidence thata modest work experience later in life canchange the behavior of older criminals.This researchhighlights both the promiseandthe limitations of employment programsfor criminal offenders. Although the Na-tional SupportedWork Demonstration Pro-ject was a bold policy experiment, it consti-tutes a weak intervention for social-scien-tific purposes. That is, the programdid notradically shift participantsalong stratifica-tion dimensions such as wealth, status, orpower. By extending a basicjob opportunity,policymakers hope to induce offenders sim-ply to desist from crime. Because of an ar-ray of political, economic, and ethical con-straints, however, the best that such pro-grams can offer is honest work for meagerwages. This study shows an age-graded re-sponse to this offer that supports a life-course conception of crime. Although pro-gramsproviding marginal obs arerelativelyunattractive to youth, they may provide theturningpoint towarda viable pathwayout ofcrime for older offenders.Christopher Uggen is Assistant Professor of So-ciology at the University of Minnesota. His gen-eral research interests include criminology, law,

and employment.More specifically, his workfo-cuses on desistance-the termination of criminalcareers. His current projects address the impactof laws that prohibit convicted felons from vot-ing (with Jeff Manza), the socioeconomic deter-minantsof illegal earnings (with Melissa Thomp-son), and offending and victimization in theworkplace.REFERENCESBachman, Jerald G. and John Schulenberg. 1993."How Part-Time Work Intensity Relates toDrug Use, Problem Behavior, Time Use, andSatisfaction among High School Seniors: Arethese Consequences or Merely Correlates?"Developmental Psychology 29:220-35.Berk, Richard A., Kenneth J. Lenihan, and PeterH. Rossi. 1980. "Crime and Poverty: Some Ex-perimental Evidence from Ex-Offenders."American Sociological Review 45:766-86.Brown, Randall. 1979. "Assessing the Effects ofInterview Non-Response on Estimates of theImpact of Supported Work." Princeton, NJ:Mathematica Policy Research.Campbell,Donald T. and Julian C. Stanley. 1963.Experimental and Quasi-Experimental De-signs for Research. Boston, MA: Houghton

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