Deterring Delinquents: A Rational Choice Model of Theft ...faculty.washington.edu/matsueda/Papers/Deterring.pdf · Deterring Delinquents: A Rational Choice Model of Theft and Violence

Rational choice theories have advanced con-siderably in the social sciences particular-

ly in economics political science and law (egMorrow 1994 Posner 1998 Sunstein 1999)In sociology especially with the popularity ofsocial capital theory rational choice has gainedtraction as an individual-level theory of moti-vation compatible with macro-level theories of

social structure (Coleman 1990) Neverthelessskepticism in sociology persists in part due tomisconceptions but more importantly due toquestions about the explanatory power of ration-al choice theories Proponents and skeptics alikeagree that a fair assessment of the theory askswhether it has paid off empirically Hechter andKanazawa (1997) conclude that some empiricalstudies support rational explanations (some-times unwittingly) in areas beyond marketbehavior but that additional research is need-ed to examine rational choice theory in a vari-ety of areas of social life and forms of socialaction

A challenging and important empirical puz-zle for rational choice theory concerns thesocial control of criminal behavior Crime is adifficult case for rational choice theory In thecase of street crime behavior is typically char-acterized as irrational and suboptimal This isin contrast to market behavior financial deci-sions and corporate crime where institution-alized norms frame decision making in theterms of rationality or optimality Indeed themediamdashand some academicsmdashcommonly por-tray street criminals as impulsive unthinking

DDeetteerrrriinngg DDeelliinnqquueennttss AA RRaattiioonnaall CChhooiiccee MMooddeell ooff TThheefftt aanndd VViioolleennccee

Ross L Matsueda Derek A KreagerUniversity of Washington University of Washington

David HuizingaUniversity of Colorado

This article examines criminal behavior from a rational choice perspective the set of

behavioral principles underlying our legal institution The authors use a subjective utility

approach and specify experiential learning models of the formation of risk perceptions

and rational choice models of theft and violence They estimate the models using panel

data on high risk youth from the Denver Youth Survey Using random effects Tobit models

of perceived risk and negative binomial models of counts of criminal acts the authors

find support for a rational choice model Perceived risk follows a Bayesian updating

model in which current risk perceptions are a function of prior risk perceptions plus new

information based on experience with crime and arrest and observations of peers Theft

and violence are a function of the perceived risk of arrest subjective psychic rewards

(including excitement and social status) and perceived opportunities

AAMMEERRIICCAANN SSOOCCIIOOLLOOGGIICCAALL RREEVVIIEEWW 22000066 VVOOLL 7711 ((FFeebbrruuaarryy9955ndashndash112222))

2714-ASR 711 filename71105-Matsueda

Direct correspondence to Ross L MatsuedaDepartment of Sociology Box 353340 University ofWashington Seattle WA 98195 (matsuedauwashingtonedu) Supported by grants from theNational Science Foundation (SES-0004323) theRoyalty Research Fund of the University ofWashington and the Office of Juvenile Justice andDelinquency Prevention (1996-MU-FX-0017) Thefunding agencies bear no responsibility for the analy-ses and interpretations drawn here This article is arevised version of a paper presented at the annualmeetings of the American Sociological AssociationAtlanta August 16ndash19 2003 The authors thankIrving Piliavin for helping to design the rationalchoice instrument used here and Kate Stovel GregPogarsky three anonymous reviewers and the ASReditor for comments on an earlier draft

Delivered by Ingenta to University of Wisconsin-Madison (cid 80004773) Ebling Lib Info Res Unit (cid 292294) matsueda

- nullTue 14 Mar 2006 000925

and uneducated and their behaviors as beyondthe reach of formal sanctions (eg Gottfredsonand Hirschi 1990) Thus a finding that streetcrimes follow rational choice principles wouldprovide strong evidence for rational choice per-spectives

Crime is an important arena for investigatingrational choice for another reason Utilitarianprinciples and their accompanying psycholog-ical assumptions undergird our legal institution(eg Maestro 1973) This connection is rootedin writings of the classical school Long agoBentham ([1789] 1948) argued that happinessis a composite of maximum pleasure and min-imum pain and that the utilitarian principlemdashthe greatest happiness for the greatestnumbermdashunderlies morals and legislation ForBentham punishment by the state constitutesone of four sanctionsmdashpolitical moral physi-cal and religiousmdashthat shape pleasures andpains Beccaria ([1764] 1963) influenced by themoral philosophers of the Enlightenmentassumed that criminal laws reflect the terms ofa social contract in which members of societyreceive protection of their rights to personalwelfare and private property in exchange forrelinquishing the freedom to violate the rightsof others Those rights are protected by the statethrough deterrence threatening potential trans-gressors with just enough punishment to out-weigh the pleasures of crime1 Beccariaattempted to reform the unjust and brutal legalsystem of eighteenth-century Europe by devel-oping a rational system that specified laws clear-ly and a priori (so individuals have fullinformation about the consequences of theiracts) eliminated judicial discretion (so all cit-izens are equal in the eyes of the law) and madepunishments certain swift and no more severethen needed to deter the public from crime

Critical legal scholars however challengethis view of the legal system They see the sys-tem as an ideological justification (manifestfunction) of modern capitalist societies whichdisguises its true essence (latent function) aslegitimizing inequitable class relations For

example Pashukanis ([1929] 1978169) devel-ops a Marxist commodity exchange theory oflaw whereby under capitalism punishmentbecomes an exchange ldquoin which the offenderlsquopays his debtrsquo and wherein crime becomes lsquoaninvoluntarily concluded contractrsquordquo (Garland1990113) For Garland (1990117) ldquothe key tounderstanding criminal law in class terms is toappreciate the ways in which particular interestsare interwoven with general onesrdquo such thatprotection of class interests are disguised asprotection of universal interests As for deter-rence he argues that ldquothe simple fact is that nomethod of punishment has ever achieved highrates of reform or of crime controlmdashand nomethod ever willrdquo (Garland 1990228) Ourpurpose is not to adjudicate between views ofthe legal system as a mechanism for achievingthe greatest happiness for the greatest numberas Bentham ([1789] 1948) and Beccaria ([1764]1963) argue or as a tool for exercising ldquoclassterrorrdquo and mystifying class relations asPashukanis and Garland argue Rather ourobjective is to examine the question of ration-al choice and deterrence which cuts to the coreof assumptions underlying our legal institutionDo individuals act as if they are rational andrespond to incentives as rational choice andclassical theorists assume Or does conformi-ty lie principally outside the purview of pun-ishment and rational choice as Garland andother sociologists of law maintain

Recent treatments of rational choice anddeterrence follow Beckerrsquos (1968) seminal arti-cle outlining an expected utility model of crim-inal decision making (see McCarthy 2002)Beckerrsquos model specifies that individuals willengage in crime when expected utility fromcommitting crime is greater than expected util-ity from not committing crime The utility func-tion in turn is a function of two additivecomponents (1) the utility of returns from crime(the ldquobootyrdquo) weighted by the objective proba-bility of getting away with it plus (2) the util-ity of returns minus the punishment eachweighted by the objective probability of gettingcaught and punished

Early empirical studies of Beckerrsquos modeluse statistical models of aggregate crime ratesand focus on deterrent effects of risk of impris-onment (measured by imprisonment per capi-ta) or risk of arrest (measured by arrests perreported crimes) Ehrlich (1973) found deterrent

9966mdashmdashndashndashAAMMEERRIICCAANN SSOOCCIIOOLLOOGGIICCAALL RREEVVIIEEWW


1 The form of US criminal laws reflects utility anddeterrence theories rather than moralizing aboutillegal acts the statutes stipulate simply and dis-passionately that whosoever engages in a certaincrime will be punished with a certain sanction


- nullTue 14 Mar 2006 000925

effects of imprisonment but scholars criticizedhim for using simultaneous equation modelswith implausible solutions to the identificationproblemmdashsuch as assuming socioeconomic sta-tus (SES) region and population age have noeffect on crime (Nagin 1978) More recent workhas unearthed deterrent effects using more plau-sible instrumental variables For exampleSampson and Cohen (1988) following the workof Wilson and Boland (1978) use aggressivepolicing as an instrument for risk of arrest (arrestper reported crime) Also Levitt (1997) uses thetiming of mayoral elections as an instrument fornumber of police per capita (see also Marvelland Moody 1994) For a review of this work seeNagin (1998)

These tests of the deterrence hypothesisassume that actors know the objective certain-ty of arrest and imprisonment This is perhapsthe most questionable assumption of expectedutility models Subjective expected utility mod-els in contrast relax this assumption replacingthe single known objective probability with adistribution of subjective probabilitiesSubjective utility models however are stillrational models because the statistical mean ofthe subjective probability distribution isassumed to fall on the value of the objectiveprobability (Nagin 1998) Moreover by speci-fying that subjective costs and returns are thekey determinants of criminal decision makingsubjective expected utility models open newpuzzles and new ways of testing rational choiceIn particular we can use direct measures ofsubjective expected costs and returns and exam-ine whether they conform to rational models oftheir causes and consequences

A crucial subjective cost of crime is the per-ceived risk of formal sanction The importantpolicy and theoretical question is that of deter-rence Do individuals with higher perceptionsof the risk of punishment commit fewer crimi-nal acts Here prior research is equivocalLongitudinal panel studies of perceived riskand self reported delinquency find weak or non-significant deterrent effects (Paternoster 1987)whereas vignette studies of perceived risk andintentions to commit crime find significantdeterrent effects (Nagin 1998) We reassessthese results in this article using new data onhigh risk youth

But returns to crime may be just as importantas costs Here a thorny issue involves specify-

ing the relevant perceived rewards of crime forindividuals located within specific structuraland cultural contexts Obviously pecuniaryreturns are important for monetary crimes Butethnographic research suggests that psychicreturns such as excitement thrills and statuswithin peer groups may be even more impor-tant Identifying the psychic rewards from ordi-nary street crimes is essential to testing a rationalchoice theory of crime Moreover someresearch suggests that perceived rewards dom-inate costs in criminal decision makingmdashpre-sumably because criminals discount formalpunishment due to its long time horizon (egCarroll 1978 Piliavin et al 1986) We revisit andreevaluate this finding here

From both a theoretical and policy stand-point a question as important as predictingcriminal behavior concerns the formation ofrisk perceptions Are risk perceptions related toactual experiences of punishment (Nagin 1998)Rational choice theory assumes that risk per-ceptions are rooted at least to some degree inreality Deterrence theory and policy also makethis assumptionmdashif actual punishments fail toincrease perceptions of risks punitive policieswill fail (Jacob 1979) Thus modeling the for-mation of risk perceptions which requires aninformation theory of risk formation gives usanother way of testing both rational choice anddeterrence theories

FFOORRMMAATTIIOONN OOFF RRIISSKK PPEERRCCEEPPTTIIOONNSSAANN EEXXPPEERRIIEENNTTIIAALL LLEEAARRNNIINNGG MMOODDEELL

BBAAYYEESSIIAANN LLEEAARRNNIINNGG

An important question in the sociology of riskand decision making concerns how individualsform their perceptions of risk (Tallman andGray 1990 Heimer 1988) This literature con-trasts a Bayesian learning process with alter-native models based on heuristics and biases(Kahneman and Tversky 1972) According to aBayesian learning modelmdashbased on Bayesrsquoprobability theoremmdashindividuals begin with aprior subjective probability of an event such asthe risk of arrest based on all the informationthey have accumulated to that point They thencollect or come into contact with new infor-mationmdashsuch as learning of a friendrsquos arrest orbeing arrested themselvesmdashwhich they use toupdate their probability estimates This newprobability or ldquoposterior probabilityrdquo is a com-

RRAATTIIOONNAALL CCHHOOIICCEE DDEETTEERRRREENNCCEE AANNDD DDEELLIINNQQUUEENNCCYYmdashmdashndashndash9977



- nullTue 14 Mar 2006 000925

bination of the prior probability and the newinformation Applied to the formation of per-ceived risk we would expect that updated risksare a function of prior risk plus new informa-tion Bayesian learning has been examined byeconomists to model consumersrsquo evaluationsof new products (McFadden and Train 1996) bypolitical scientists to model voting and partyaffiliation (Achen 1992) and by psychologiststo model court testimony scenarios (Winterfeldtand Edwards 1986) Moreover Nagin (1998)suggested that this model can be applied to theformation of perceptions of the risk of arrestSuch a process is consistent with general sociallearning theories of crime (eg Akers 1998)

Cognitive psychologists such as Tversky andKahneman (1974) have criticized this view oflearning They use experimental evidence tofind that actors depart from Bayesian learningby using cognitive shortcuts or heuristic ruleswhich can lead to biased and distorted risk per-ceptions (Tallman and Gray 1990) For exam-ple individuals tend to update using onlyinformation that is easily or quickly retrievedfrom memory so that dramatic and vivid events(such as arrest) may swamp other mundanesources of information (such as experiences ofothers) Individuals also tend to anchor theirestimates on initial probabilities rather thanupdating properly in light of new informationas when a person ignores the arrest of com-panions in forming risk perceptionsFurthermore individuals tend to rely on stereo-types while ignoring population distributionssuch as when a person overestimates the prob-ability that a mother is black when given theinformation that she is a teenage mother (for-getting that whites are disproportionately rep-resented in the population) (Heimer 1988)

In the case of certainty of sanction individ-uals often rely on stereotypes depicted in themedia in which criminals are caught and arrest-ed (Andenaes 1975 Parker and Grasmick 1979)Indeed the media often manipulate this stereo-typemdasheg with television shows like Cops(2005)mdashwhich may increase deterrent effectsparticularly among naiumlve individuals with noexperience with the criminal justice systemResearch suggests that such naiumlve individualstend to overestimate the likelihood that theywill be arrested if they commit crimes (egJensen 1969) a phenomenon Tittle (198067)terms ldquothe shell of illusionrdquo

Finally individuals fall prey to the gamblerrsquosfallacy which occurs when a person assumesthat a departure from what happens in the longrun will be corrected in the short run For exam-ple if nine coin flips in a row have come up tailsindividuals might think they are ldquoduerdquo for acoin flip to come up heads Consequently indi-viduals who continuously get away with crimemay think they are ldquoduerdquo for an arrest or thosewho experience a string of arrests may thinkthey are ldquoduerdquo to get away with crime (Pogarskyand Piquero 2003)

For our purposes such heuristics suggest thatthe formation of perceived risks may not followa strictly Bayesian learning perspectiveIndividuals may begin by overestimating theirrisk of arrest remain anchored on their baselinevalues and fail to update or update only onvivid events such as dramatic personal experi-ences

FFOORRMMAATTIIOONN OOFF PPEERRCCEEIIVVEEDD RRIISSKK OOFF FFOORRMMAALL

SSAANNCCTTIIOONN

The perceived risk of punishment for commit-ting a crime makes a good case study forBayesian updating because it is a clearly delin-eated risk and applies to all members of socie-ty We specify three important sources ofinformation from which individuals update theirperceptions of risk of arrest (1) their own expe-riences with offending including getting arrest-ed and avoiding arrest (2) their knowledge offriendsrsquo experiences with offending and (3)their social structural location Drawing on aBayesian learning perspective we develophypotheses about each of these sources of infor-mation

An updating model (see Figure 1 bold lines)begins with a prior or baseline estimate of per-ceived risk of punishment

Hypothesis 1 Prior Perceived Risk Future per-ceived risk is a positive function of priorperceived risk plus any updating

The key source of information for perceivedrisk is an individualrsquos own experiences withcrime and punishment We use two concepts tocapture these experiences The first ldquoexperi-enced certaintyrdquo refers to an individualrsquos rate ofarrests per number of reported crimes We usethis ratemdashrather than the absolute number ofarrestsmdashbecause the marginal effect of getting




- nullTue 14 Mar 2006 000925

arrested on perceived certainty of arrest dimin-ishes with each undetected crime (Horney andMarshall 1992) This leads to our secondhypothesis

Hypothesis 2 Bayesian Learning Based onPersonal Experience with Arrest Net ofprior risk experienced certainty of arrest ispositively and monotonically associated

with perceived risk of arrest2

The second concept ldquounsanctioned offensesrdquorefers to the number of offenses committed byindividuals who have never been arrested Forthese individuals experienced certainty scoreswill be zero regardless of how many crimes theyhave committed But clearly from an updatingstandpoint each time they commit a crimemdashandget away with itmdashthey will update (lower) theirperceptions of risk

Hypothesis 3 Bayesian Learning Based onPersonal Experience with CrimeUnsanctioned offenses (crimes by never-arrested persons) are negatively and monot-onically associated with perceived risk ofarrest

Some studies offer qualified support for theBayesian learning hypothesis Parker andGrasmick (1979) find that among respondents



Figure 1 Experiential Learning Models of Perceived Risk of Arrest for Violence and Theft

Note Bold lines and boxes refer to the basic updating model other boxes and lines refer to controls

Individual

Age

Sex

Race

Family Structure

Impulsivity

Family Income t-1

Risk Preference t-1

New Information

Delinquent Peers t-1

Experienced Certainty t-1

Unsanctioned Offenses t-1

Updated Risk

Perceived Risk t

Baseline Risk

Perceived Risk t-1

Neighborhood

Concentrated Disadvantage

Mobility

Percent Black

Crime Rate

2 Our measures lack the precision to specify afunctional form for this relationship Thus we sim-ply examine the empirical distribution for monoto-nicity where the relationship may be nonlinear butis always either increasing (positive) or decreasing(negative) but never both


- nullTue 14 Mar 2006 000925

who knew of at least one burglary experiencedcertainty is positively correlated with certaintyThey also find that those with zero experiencedcertainty have higher certainty estimates andamong those with no arrests burglary is slight-ly negatively correlated with perceived cer-tainty In perhaps the strongest study ofupdating Horney and Marshall (1992) interviewincarcerated felons compute experienced cer-tainty (arrests per offense) for a variety ofoffenses and find significant effects on per-ceived certainty of arrest for the offense FinallyPogarsky Piquero and Paternoster (2004) findthat arrest increases future perceived certaintyamong high school students particularly forthose with low prior estimates of certainty Withrespect to ldquounsanctioned offensesrdquo research onthe ldquoexperiential effectrdquo finds strong effects ofprior delinquency on risk of arrest (egPaternoster 1987) Such research however failsto distinguish arrested from non-arrestedgroups and thus unsanctioned offenses fromexperienced certainty Our analyses make thisdistinction and test the hypothesis directly3

We can also test the hypothesis of a ldquoshell ofillusionrdquo in which naiumlve individuals with noexperience with offending or arrest overesti-mate the likelihood that they will be arrested ifthey commit crimes

Hypothesis 4 Shell of Illusion Compared toexperienced offenders (with non-zero expe-rienced certainty or unsanctioned offenses)naiumlve individuals overestimate the risk ofarrest

The second source of information is frompeers Over two decades ago Cook (1980) the-orized that risk perceptions are based on per-sonal experiences with crime and punishmentand the experiences of a few friends Staffordand Warr (1993) make the same point using theterms ldquopunishmentrdquo and ldquopunishment avoid-ancerdquo to refer to a personrsquos own punishmentexperience and ldquovicarious punishmentrdquo andldquovicarious punishment avoidancerdquo to refer topeer experiences (see also Piquero and Pogarsky2002) Ideally the hypothesis would be that

perceived risk of arrest will increase with greaterexperienced certainty of peers and decreasewith greater peer unsanctioned offensesResearch on peer effects however has notdirectly examined experienced certainty ofpeers but instead has examined the effect ofdelinquent peers on risk perceptionsmdashand foundsignificant effects This strategy is probably areasonable approximation given that mostoffensesmdashparticularly moderately seriousoffensesmdashgo unsanctioned and therefore riskperceptions will decline with greater delin-quency among peers (eg Parker and Grasmick1979 Piquero and Pogarsky 2002 Pogarsky etal 2004) Lacking data on experienced cer-tainty of peers we follow this strategy

Hypothesis 5 Bayesian Learning Based onVicarious Experience Delinquent peersare negatively associated with perceivedrisk of arrest

Finally risk perceptions will be related to apersonrsquos location in the social structure (Douglas1986) Social structural location will affect riskperceptions directly by structuring other sourcesof information and indirectly by affecting apersonrsquos own experiences as well as structuringpeer networks

Hypothesis 6 Social Structure and PerceivedRisk Perceived risk is shaped by locationin the social structure (eg race genderSES family structure residential stability)

These hypotheses will allow us to determinewhether or not individuals act like Bayesians informing risk perceptions But of course evenif risk formation follows updating principles arational choice perspective will not be sup-ported strongly unless perceived risks are relat-ed to behavior We now turn to this centralproposition of rational choice

RRAATTIIOONNAALL CCHHOOIICCEE DDEETTEERRRREENNCCEEAANNDD DDEELLIINNQQUUEENNCCYY

Reduced to essentials a rational choice modelof deterrence and crime specifies that an indi-vidual will commit crime if the utility of rewardsfrom crime (weighted by the probability ofobtaining the reward) outweighs the utility ofcosts (weighted by the probability of beingcaught)

PR U(Rewards) gt PC U(Costs)



3 Lochnerrsquos (2005) unpublished study finds sup-port for a learning model of perceived risk usingdata from the National Longitudinal Survey of Youth1997 (NLSY-97) and National Youth Survey (NYS)


- nullTue 14 Mar 2006 000925

where PR is the probability of receiving areward PC is the probability of a cost and U isa utility function that translates specific rewardsand costs to a common metric (eg Becker1968 Nagin and Pogarsky 2001) We treat theutilities as subjectively perceived by the actor

Long ago building on Beccariarsquos ([1764]1963) ideas about crime and punishmentJeremy Bentham ([1789] 1948) formalized andpopularized David Humersquos ([1739] 2000) con-cept of utility to refer to a calculation of pleas-ures and pains which ldquopoint out what we oughtto do as well as determine what we shall dordquoBentham hoped to categorize all pleasures andpains (eg pleasures of wealth skill powerexpectation and pains of enmity malevolenceill name and the senses) and measure theirvalue using attributes of intensity durationcertainty propinquity fecundity purity andextent Kahneman Wakker and Sarin (1997)term Benthamrsquos concept ldquoexperienced utilityrdquoto contrast it to ldquodecision utilityrdquo favored byeconomists and decision theorists The lattereschews direct measurement of utility arguingthat utility cannot be observed let alone meas-ured It opts instead to infer utility from thecharacteristics of behavioral choices and thenuse that inferred utility to predict future behav-ior We follow Kahneman et al (1997) and thetradition of Bentham and measure utility direct-ly using survey data We use a straightforwardmeasure of how good or bad the cost or rewardwould be to the individual to indicate perceivedutility and weight it by the probability of the costor reward The trick is to specify from the stand-point of the actor the relevant costs and returnsof crime that affect criminal behavior

PPEERRCCEEIIVVEEDD CCOOSSTTSS OOFF CCRRIIMMEE

The perceived costs of crime are well estab-lished and include formal sanctions such asarrest conviction jail or imprisonment Formoderately serious crimes research has focusedon the perceived certainty of arrest perhapsthe most salient cost Most cross-sectionalvignette (scenario) studies of perceived risk andself-reported future intention to commit crimefind consistent deterrent effects (eg Klepperand Nagin 1989 Nagin and Paternoster 1994)4

In contrast most research using panel studies ofself-reported crime find modest or null effectsof perceived certainty on crime among collegestudents (see reviews by Williams and Hawkins1986 and Paternoster 1987) and serious offend-ers (eg Piliavin et al 1986)5 We will use morerefined measures of both perceived risk and ofself-reported delinquency and reassess this lat-ter finding

Hypothesis 7 Deterrence Crime is reduced byperceptions of greater risk of formal sanc-tion weighted by perceived utility of thesanction

In addition to the cost of arrest there areopportunity costs to offendingmdashthat is oppor-tunities forgone by virtue of crime For adoles-cents and young adults structural opportunitycosts include schooling and work (eg Sullivan1989 Sampson and Laub 1993) Youth who areperforming well in school and who see school-ing as an avenue to future status and pecuniaryreturns will be less likely to risk committingcrime Similarly youth who seek employmentwill be less likely to jeopardize their legitimateearnings by committing crime With his socialcontrol theory of crime Hirschi (1969) essen-tially specified opportunity costs as the mech-anism by which commitment to conventionallines of action reduced delinquency and foundthat school performancemdashbut not employ-mentmdashreduced delinquency Subsequentresearch consistently finds negative effects ofschool performance on delinquency (egJenkins 1995 Stewart 2003) Research findshowever that work exerts a positive effect ondelinquency during adolescence and this effectis stronger for individuals working more than 20hours a week (eg Steinberg Fegley andDornbusch 1993 Ploeger 1997 Wright Cullenand Williams 1997) These effects persist whencontrolling for covariates that might select youthinto jobs (Staff and Uggen 2003) but not in afixed-effects panel model (Paternoster et al2003) This leads to the following hypothesis



4 In vignette studies respondents are given sce-narios of crime situationsmdashwhere characteristics of

the scenario are randomly assignedmdashand asked torate the risk of arrest (independent variable) as wellas their hypothetical intention to commit the depict-ed crime (dependent variable)

5 Lochner (2005) also finds support for the deter-rence hypothesis using cross-sectional data from theNYS and longitudinal data from the NLSY-97


- nullTue 14 Mar 2006 000925

Hypothesis 8 Opportunity Costs Crime isreduced by opportunity costs includingschooling and work

PPEERRCCEEIIVVEEDD RREETTUURRNNSS TTOO CCRRIIMMEE

Returns to crime usually refer to income whichapplies only to monetary crimes and which istypically assumed to be confiscated upon for-mal sanction (and therefore modeled by replac-ing PR with 1 ndash PC) For most crimes howevercriminological research has identified psychicreturns as key determinants of criminal deci-sions A long history of ethnographic researchin criminology has identified peer group statusas important in criminal decision making Forexample classical subcultural theories arguethat for inner-city youth blocked from attainingconventional status crime provides an alterna-tive avenue for status in the eyes of a personrsquospeers (eg Cloward and Ohlin 1960 Short andStrodtbeck 1965) More recent ethnographicresearch (eg Horowitz 1983 Anderson 1999)on inner-city Chicano and black youth findsthat violence and crime often is a way of attain-ing respect and honor among a personrsquos refer-ence group ldquoYou do it rsquocause you want to becoolrdquo (Pinderhughes 1997134) Katz (1988)argues that ldquobeing coolrdquo is an important elementof adolescent subcultures and a part of being ldquoabadassrdquo which is associated with street crimeand violence In his autobiographical account ofa potentially violent confrontation in which heldquocouldnrsquot resist the temptation to demandrespectrdquo Nathan McCallrsquos (1994378) thoughtprocess captures the role of rewards and statuswithin a rational choice framework

In a fraction of a second I calculated the possibil-ities and weighted the potential for loss and gainHe must be armed Fistfighting went out with Afropicks and platform shoes Even if by chance home-boy is an old-school rumbling man Irsquoll win no tro-phies or awards for throwing down with him I toldmyself Nathan let it ride Walk away from this onewhile you can

Recent ethnographic accounts of propertycrimes find that excitement and thrills are ubiq-uitous and second only to monetary returns asimportant motivators (eg Tunnell 1992 Frazierand Meisenhelder 1985) Jacobs (200029) findsthat robbers of drug dealers talk about the kicksand highs from robbery

ldquoItrsquos like an adrenaline rushrdquo Loony Ass Nigga pro-claimed ldquoSome people go bungee jumping somepeople go sky diving I || kick [drug] houses inrdquo(p 36)

Katz (1988) argues that criminals are oftenseduced into crime by the prospect of excitementand kicks or what he terms ldquosneaky thrillsrdquoSneak thieves are often obsessed with ldquogettingoverrdquo and the ldquoexcitement and thrillrdquo from suc-ceeding If caught and arrested they expressshock subsequently treat their theft as ldquorealcrimerdquo and typically end their deviant career toavoid commitment to a deviant identity In con-trast professional shoplifters see themselves asmembers of criminal subcultures and view theirarrest as a ldquocost of doing businessrdquo (Katz198866) Our models will draw on this litera-ture to specify psychic returns to crime such asgetting excitement and thrills (see McCarthy andHagan 2005) being seen as cool and feelinggood about oneself and hypothesize that theyaffect crime

Hypothesis 9 Psychic Returns to CrimeCriminal behavior is associated with per-ceived probability of excitement and socialstatus from crime weighted by perceivedutility of the excitement or status

CCRRIIMMIINNAALL OOPPPPOORRTTUUNNIITTIIEESS

Even if a person has determined that the rewardsto crime outweigh the costs that person cannotcommit a crime until an objective opportunityis not only present but also perceived Withtheir routine activities theory Cohen and Felson(1979) specify that criminal events occur at theintersection of motivated offenders suitabletargets and the absence of capable guardiansEmbedded in this theory is a rational motivat-ed actor seeking targets of theft or violencewhile avoiding detection Indeed Clarke andCornish (1985) specify a rational choice theo-ry of burglary in which perceived costs andbenefits lead to a state of readiness to commitburglary which interacts with an opportunitymodel of seeking suitable targets lacking capa-ble guardians (to use the language of routineactivities) Thus from a rational standpoint theimportant concept is the actorrsquos perception ofopportunities to get away with crime whichaffects future crimes




- nullTue 14 Mar 2006 000925

Hypothesis 10 Criminal Opportunities Criminalbehavior is increased by perceptions ofopportunities to get away with crime

Research from a limited rationality perspectivesuggests that criminal behavior is associatedwith criminal opportunities and rewards butnot costs Perhaps criminals have short timehorizons act on the basis of immediate oppor-tunities and tend to discount long-term conse-quences such as formal sanction (eg Wilsonand Herrnstein 1985)

Hypothesis 11 Limited Rationality andDiscounting Criminal behavior is associ-ated with perceptions of immediate crim-inal opportunities and rewards but not byperceptions of future punishment

Finally deterrence theorists have traditionallyargued that instrumental crimes such as theftburglary and robbery are more susceptible torationality and deterrence than expressivecrimes such as violence vandalism and dis-orderly conduct (eg Chambliss 1967 Zimringand Hawkins 1973) McCarthy (2002) arguesthat this distinction is blurred in reality and thatviolence is often used instrumentally to gain sta-tus (eg Short and Strodbeck 1965 Anderson1999) while theft can be expressive (eg Cohen1955) We treat this as an empirical question anddistinguish between theft and violent acts

Hypothesis 12 Instrumental versus ExpressiveCrimes Rational choice and deterrencehave stronger effects on theft than violence

DDAATTAA AANNDD MMEEAASSUURREESS

TTHHEE DDEENNVVEERR YYOOUUTTHH SSUURRVVEEYY

To test our models we use data collected byHuizinga and colleagues as part of the DenverYouth Survey (DYS) a longitudinal study ofdelinquency and drug use in high risk neigh-borhoods in Denver To identify high risk neigh-borhoods they cluster analyzed census blockgroups based on census variables (eg familystructure ethnicity SES housing mobilitymarital status and age composition) identifiedeleven clusters and selected the three mostsocially disorganized clusters They then select-ed those block groups within each cluster thatfell within the top one-third of arrest rates basedon reports of the Denver Police Department

The resulting sample is representative of neigh-borhoods at high risk of delinquency wherehigh risk is defined as residing in socially dis-organized high-crime neighborhoods

Using vacancy and completion rates theinvestigators selected 20300 of 48000 enu-merated households drew a stratified proba-bility sample of households proportional topopulation size and then used a screening ques-tionnaire to identify appropriately aged respon-dents (ie 7 9 11 13 or 15 years old) Thisprocedure yielded a sample of 1528 complet-ed interviews in the first wave constituting acompletion rate of 85 of eligible youths (seeEsbensen and Huizinga 1990 for more details)Attrition rates were relatively low (7ndash9 percent)across the first five waves of data used hereRespondents age 11 years and older (at eachwave) received a youth questionnaire with ourrational choice measures We use data from 1113- and 15-year-old cohorts between waves2ndash5 9-year-olds between waves 3ndash5 and 7-year-olds between waves 4ndash5 The total sampleincludes 1459 respondents and 3298 person-years after accounting for missing values on thedependent variables6

This sampling strategy has three implica-tions for our analyses First we have adequatevariation on our key rational choice and crimevariables necessary for powerful tests of ourrational choice hypotheses Second we candraw inferences to the important population ofyouth residing in high risk neighborhoods butwe must be careful to avoid generalizing to low-risk neighborhoods Third we expect neigh-borhood characteristics related to socialdisorganization to have less variability in oursample and less explanatory power in our mod-els

CCOONNCCEEPPTTSS AANNDD MMEEAASSUURREESS

Descriptive statistics appear in Table 1 descrip-tions of our measures and concepts appear in theAppendix (Table A1) We include contextualand background variables describing the struc-tural location of individuals To measure neigh-



6 We had slight (less than ten percent) missingvalues on some variables we use a conditional meanimputation which does not change the results appre-ciably


- nullTue 14 Mar 2006 000925



Table 1 Variable Descriptive Statistics

Minimum Maximum Mean or Proportion SD

VariablesmdashFemale 000 100 047 050mdashAge 1100 2000 1508 241mdashBlack 000 100 033 047mdashHispanic 000 100 045 050mdashNo Siblings 000 100 053 050mdashConcentrated Disadvantage ndash167 211 0003 087mdashProportion Black 001 099 033 033mdashResidential Stability ndash195 269 003 085mdashCrime Rate 1984a 141 480 233 090mdashHigh Impulsivity 000 100 028 045mdashBiological Parentstndash1 000 100 031 045

mdashParent(s) on Welfaretndash1 000 100 013 031

mdashFamily Income ($1000s)tndash1 006 26000 833 1532

mdashMissing Parent Reporttndash1 000 100 015 036

mdashSelfndashReported Risk Takertndash1 000 100 067 045

Variables Specific to Perceived Risk of Arrest ModelsmdashViolence Perceived Risk of Arrestt 000 10000 4360 3470

mdashTheft Perceived Risk of Arrestt 000 10000 4660 3640

mdashPrior Violence Perceived Risk of Arrest tndash1 000 10000 4411 3509

mdashPrior Theft Perceived Risk of Arresttndash1 000 10000 5030 3643

mdashExperienced Certaintyt

mdashmdashgt00ndash05 000 100 012 032mdashmdash06ndash25 000 100 011 031mdashmdash26ndash10 000 100 011 031mdashUnsanctioned Offensest

mdashmdashLow Offending (0ndash2) 000 100 010 030mdashmdashMedium Offending (3ndash9) 000 100 010 030mdashmdashHigh Offending (ge10) 000 100 013 034mdashPeer Violencet 100 500 193 091

mdashPeer Theftt 100 500 160 082

Variables Specific to Behavior Models tmdashViolence 000 30000 118 815mdashTheft 000 34000 126 1169mdashViolence Opportunities 100 500 193 119mdashTheft Opportunities 100 500 126 142Variables Specific to Behavior Models tndash1mdashGrades 000 500 352 106mdashEmployed 000 100 037 047mdashNot in School 000 100 003 016mdashPrior Violence (logged) 000 669 031 079mdashPrior Theft (logged) 000 690 032 080mdashPolice Contact 000 5200 059 209mdashViolence Perceived Risk of Arrest 000 500 204 169mdashViolence Excitement 000 500 063 124mdashViolence Coolness 000 500 073 119mdashTheft Perceived Risk of Arrest 000 500 237 177mdashTheft Excitement 000 500 044 073mdashTheft Coolness 000 500 051 091

Note Sample sizes for dependent variables differ slightly due to a few missing values Pooled waves 3ndash5 N =3582 SD = standard deviation For independent variables sample size is constant due to conditional meanimputationa Total number of crimes reported to police in 1984 per 10 residents by neighborhood


- nullTue 14 Mar 2006 000925

borhood disorganization we factor-analyzedsix 1990 census block group variables identi-fied two dimensions found in previous studiesand created two scales residential stability andconcentrated disadvantage We treat proportionblack in the census block group as a distinct vari-able Crime rate 1984 is the total number ofcrimes reported to police in 1984 per 10 resi-dents by neighborhood For our individual back-ground variables female age family structureblack and Hispanic origin are taken from youthreports while income and welfare are takenfrom parent reports For the 11 percent missingparent reports we impute the mean and use adummy variable to control for the effect of miss-ing values Following studies of impulsivityand antisocial behavior high child impulsivityis measured by four items from parent reportsin 1988 summed to form an index with relia-bility = 72

Our key conceptsmdashrisks returns and oppor-tunitiesmdashare measured from youth reports7

For models of perceived risk of arrest we usetwo sets of variables to measure experientiallearning First is experienced certainty meas-ured by the ratio of the number of times everarrested or questioned by police to the numberof all self-reported crimes in the past year8

Following Horney and Marshall (1992) we cre-ate three dummy variables to represent threeintervals derived from the distribution of the data(gt00ndash05 06ndash25 26 ndash10) with non-offend-ers (zero self-reported crimes) as the omittedcategory (see Table 1) The dummy variableswill allow us to test the updating hypothesisthat experienced certainty is positively andmonotonically related to perceived certaintySecond are unsanctioned offenses measuredby the number of self-reported offenses com-mitted by those who have not been questionedor arrested by police We again categorize thesevariables into three intervals (0ndash2 3ndash9 10 andhigher) based on the distribution of the data cre-ate three dummy variables (with non-offenders

again the omitted category) and test for a neg-ative monotonic relationship Our risk modelsalso include three-item indexes of peer theft (= 82) and peer violence ( = 84) to captureinformation from the experience of friends

For models of theft and violence we use foursets of variables to measure perceived risks andrewards perceived opportunities risk prefer-ences and opportunity costs Our centralexplanatory variables are perceived costs andreturns to crime For each cost and return wemeasure perceived utility by asking how goodor bad it would be and subjective probability byhow likely it would result from theft or vio-lence Thus our costs are the probability ofarrest for committing theft (violence) weightedby how good or bad an arrest would beSimilarly our rewards are the probability ofgetting excitement from theft (violence) andthe probability of being seen as cool from com-mitting theft (violence) each weighted by howgood or bad excitement or coolness would be9

Note that for theft we are controlling for mon-etary rewards to crime by specifying a monetaryamount ($10) in the question

To measure crime opportunities we use indi-cators of perceived opportunities to committheft (violence) and get away with it (egPiliavin et al 1986 Hagan and McCarthy 1997)The clause ldquoget away with itrdquo implies thatconsistent with a rational choice model oppor-tunities are capturing an interaction betweenperceived opportunities to commit crime and theperceived certainty of arrest10 We measure riskpreference (or taste for risk) in a straightforwardway ldquoDo you agree with the statement lsquoI liketo do daring thingsrsquordquo (eg Hagan Simpson



7 Measures in the rational choice section of theDYS were designed by Ross Matsueda and IrvingPiliavin in collaboration with David Huizinga andFinn Esbensen

8 We also constructed this variable using arrests inthe past year to correspond to the time frame of self-reports and found similar results

9 We also examined alternate measures of costs(chances you would end up in jail or training schoolchances your parents would find out) and rewards(chances you would feel good about yourself) whichare related to theft and violence but not quite asstrongly as those we report

10 The probability of getting away with crimeP(Crime not Arrest) is related to the certainty ofarrest P(Arrest|Crime) according to the followingequation P(Crime not Arrest) = P(Crime) [1 ndashP(Arrest|Crime)] where the second term is the prob-ability of crime times one minus the perceived cer-tainty of arrest This assumes that the modelconditions on propensity to deviate so that P(Crime)is perceived objective opportunity

not

not


- nullTue 14 Mar 2006 000925

and Gillis 1987) We use two measures of oppor-tunity costs of crime grades (measured by self-reported grade point average) and employment(measured by a dummy variable indicatingwhether or not the youth is employed) Weinclude a dummy variable for being out ofschool (and thus missing values on grades)

To distinguish instrumental from expressivecrimes we estimate separate models of theft andviolence For models of risk perceptions ourdependent variable is the probability of beingpicked up by the police for theft (violence)measured on a probability scale ranging from0 to 100 incremented by intervals of 10 Test-retest correlations (not shown) which confoundtrue change with unreliability show greaterchange in risk perceptions for younger cohorts(25 for 11 year olds) than older cohorts (45 for15 year olds) as expected The average of about33 probably reflects moderate true change andreasonable reliability For example if true sta-bility 21 were 50 and reliability xx were 70we would expect a test-retest correlation r21 of35 since r21 = 21 xx

12 xx12 (for temporal dis-

tributions see our ASR Online Supplement tothis article at httpwww2asanetorgjournalsasr2005toc049html) For models oftheft (violence) our dependent variable is asum of seven (three) standard self-report itemssummed to provide a count of offenses for thepast year (see Appendix) We control for priortheft (violence) in all models11

MMOODDEELLSS OOFF BBAAYYEESSIIAANN LLEEAARRNNIINNGGAANNDD PPEERRCCEEIIVVEEDD RRIISSKK OOFF AARRRREESSTT

To test our Bayesian updating model of per-ceived risk of arrest we specify a substantivemodel diagrammed in Figure 1 This modelcontrols for exogenous neighborhood variables(concentrated disadvantage residential mobil-ity percent black and the crime rate in 1984)and individual characteristics (age sex racefamily structure impulsivity and risk prefer-ence) It specifies perceived risk at time t as a

function of prior risk at time tndash1 and then intro-duces three pieces of new information eachmeasured at tndash1 to test the updating hypothe-sis experienced certainty unsanctioned offens-es and delinquent peers

SSTTAATTIISSTTIICCAALL MMOODDEELLSS RRAANNDDOOMM--EEFFFFEECCTTSS

TTOOBBIITT MMOODDEELLSS

To estimate models of perceived risk of arrestwe use random-effects Tobit models with laggedregressors Our models can be specified as fol-lows If we let yit be our observed measure ofperceived risk of arrest for person i at time t ourmodels take the following form

yit = i + zi + xitndash1 + yitndash1 + it

u if yit ge u

where yit = yit if u gt y

it gt 1

1 if yit le 1

and i is unobserved heterogeneity which variesacross individuals but not time and arecoefficients y

it is a latent continuous (unbound-ed) measure of perceived risk zi is a vector oftime-invariant covariates xitndash1 is a vector oftime-varying lagged covariates yitndash1 is observedlagged perceived certainty it is a disturbanceterm (assumed normal with constant variance)and u and 1 are upper and lower thresholdsrespectively

Our Tobit models address violations of threekey assumptions of the usual linear regressionmodel First the dependent variable perceivedrisk is measured in probabilities which boundthe scales at zero and 100 Consequently linearmodels will yield downwardly biased coefficientestimates due to floor and ceiling effects whenthe data clump at zero or 100 Therefore we usea two-limit censored regression or Tobit modelwhich takes into consideration the propensity tobe censored at zero or unity (see Maddala 1983Long 1997)12 Second our pooled person-year



11 We logged the lagged dependent variable so thefunctional form approximates linearity (and add oneto each observed value before logging to deal withzero values) This results in stronger lagged effectsand more conservative estimates of effects of othervariables

12 We treat the floor and ceiling effects as gener-ated by problems in the measurement scale ratherthan generated by a true probability scale that allowsnegative values or values exceeding unity We exam-ine the possibility that the process generating non-bounded values differs from the process generating


- nullTue 14 Mar 2006 000925

data are dependent across individuals due tounobserved heterogeneity We use randomeffects models which assume i is uncorrelat-ed with zi and xitndash1 to correct for such depend-ence and obtain consistent and asymptoticallyefficient estimates13 Third our independent(experienced certainty and peer delinquency)and dependent variables (perceived certainty)could be reciprocally related creating an endo-geneity problem Therefore we use a three-wave panel model with a lagged endogenouspredictor (prior perceived certainty) yitndash1 tocontrol for stability or state dependence in ouroutcome variable and lagged time-varyingcovariates xitndash1 to ensure that the temporalspecification of our models is consistent withour theoretical specification Our random-effects model helps address potential bias inthe estimate of the effect of yitndash1 and thereforeother effects as well by building in equal-cor-related (regardless of lag) autocorrelated dis-turbances

EESSTTIIMMAATTEESS OOFF PPAARRAAMMEETTEERRSS OOFF PPEERRCCEEIIVVEEDD

RRIISSKK MMOODDEELLSS

Table 2 presents estimates of our models ofexperiential learning and perceived risk of arrestfor theft and violence Model I (columns 1 and2) our reduced-form equation reveals thatfemales and younger respondents perceive ahigher risk of arrest for both theft and violence(lines 2 and 3) On average females believethe chances of arrest for theft are eleven per-centage points higher than do males and for vio-lence seven points higher (eg Blackwell andEschholz 2002) Each year of age is associatedwith a decrease of four percentage points in

perceived risk for theft and one percentage pointfor violence Youth without siblings perceive alower risk of arrest for violence the effect isnon-significant for theft (line 4) With oneexception our neighborhood contextual vari-ables fail to exert significant effects on cer-tainty probably because we sampled the mostdisorganized and high crime neighborhoodsThe exception is residential stability which isassociated with lower perceived risk of arrest fortheft (line 5)

Model II (columns 3 and 4 Table 2) whichadds our lagged control variables nearly dou-bles the pseudo-R2 for both theft and violence(compare Models I and II) and reveals two sig-nificant effects14 First on average respondentswho report a preference for risk perceive a lowerrisk of getting arrested for both theft and vio-lence (line 9) Second the one-year laggedeffects of perceived risk of arrest are substan-tial confirming the importance of controllingfor stability or state dependence (line 10)

Model III (columns 5 and 6 Table 2) adds ourexperiential learning variables (nearly doublingthe pseudo-R2s to 11 for violence and 26 fortheft) and tests our Bayesian learning hypothe-ses We turn first to experienced certainty (therate of arrest per offense) and find evidence forupdating using a joint test we reject the nullhypothesis that net of prior perceived riskexperienced certainty has no effect on perceivedrisk for theft (2 = 2374 df = 3 p lt 001) andviolence (2 = 2634 df = 3 p lt 001) The sig-nificant negative coefficients (lines 11ndash13) sup-port the ldquoshell of illusionrdquo hypothesis 4 inwhich naiumlve individuals who have neitheroffended nor been arrested will overestimatethe certainty of arrest relative to arrested offend-ers and not the gamblerrsquos fallacy (in whichcoefficients would be positive)

Moreover the three dummy variables exhib-it a monotonic relationship with certaintymdashthat is as hypothesized from Bayesian learningas one moves from low experienced certainty tohigh perceived certainty increases monotoni-cally 1 lt 2 lt 3 (lines 11ndash13 Table 2) Thisis shown graphically in Figure 2 (compare bars



variation in positive probabilities using Craggrsquos(1971) model (Greene 2003) see ASR OnlineSupplement to this article (httpwww2asanetorgjournalsasr2005toc049html) On criticisms of theTobit model see Raftery (2001) and Maddala (1983)

13 The Tobit model is not robust to violations ofassumptions of normally distributed and homoscedas-tic disturbances Thus we examine these assumptionsin sensitivity analyses (see ASR Online Supplement)We also examine the robustness of assuming i isuncorrelated with zi and xitndash1 of using yitndash1 versusyitndash1 as our endogenous regressor (see Wooldridge2002) and of allowing serially correlated errors inthe presence of a lagged endogenous predictor

14 The pseudo-R2 is the variance of the predictedconditional mean to the variance of the dependentvariable and is bounded by zero and one (Greene2002E21ndash12)


- nullTue 14 Mar 2006 000925



Tab

le 2

R

ando

m E

ffec

ts T

obit

Mod

els

of P

erce

ived

Cer

tain

ty o

f Arr

est (

Wav

es 3

ndash5)

Mod

el I

Mod

el I

IM

odel

III

The

fta

Vio

lenc

ebT

heft

aV

iole

nceb

The

fta

Vio

lenc

eb

Var

iabl

esmdash

Inte

rcep

t99

08

534

1

681

3

429

0

710

3

432

3

(82

1)(7

53)

(86

3)(7

73)

(86

0)(8

01)

mdashFe

mal

e11

18

6

72

9

00

5

25

586

3

36

(22

9)(2

11)

(20

0)(1

94)

(19

9)(1

96)

mdashA

ge

ndash38

9

ndash10

4ndash2

51

ndash0

45

ndash16

1

021

(0

44)

(04

0)(0

43)

(03

9)(0

43)

(04

1)mdash

No

Sib

ling

sndash3

25

ndash45

0ndash2

62

ndash45

8ndash2

92

ndash44

7(2

29)

(21

2)(2

03)

(19

6)(1

97)

(19

4)mdash

Res

iden

tial

Sta

bili

tyndash3

20

ndash11

3 ndash2

90

ndash11

1 ndash2

54

ndash09

6 (1

60)

(14

8)(1

39)

(13

4)(1

34)

(13

3)L

agge

d V

aria

bles

tndash1

mdashB

iolo

gica

l Par

ents

mdashmdash

229

2

54

022

1

24

(22

2)(2

11)

(21

7)(2

10)

mdashPa

rent

(s)

on W

elfa

remdash

mdash1

03

022

2

40

092

(2

95)

(27

2)(2

90)

(27

0)mdash

Fam

ily I

ncom

e ($

1000

s)mdash

mdashndash0

01

ndash00

4 0

00

ndash00

3 (0

06)

(00

6)(0

06)

(00

6)mdash

Ris

k P

refe

renc

emdash

mdashndash6

38

ndash6

40

ndash3

68

ndash44

1(2

04)

(18

9)(2

02)

(18

9)mdash

Pri

or P

erce

ived

Ris

k of

Arr

est

mdashmdash

029

017

027

016

(00

4)(0

04)

(00

4)(0

04)

Var

iabl

es a

t Tim

e t

mdashE

xper

ienc

ed C

erta

inty

mdashmdash

gt0

0ndash0

5mdash

mdashmdash

mdashndash1

573

ndash15

46

(3

46)

(32

7)mdash

mdash0

6ndash2

5mdash

mdashmdash

mdashndash8

42

ndash1

156

(33

1)(3

15)

mdashmdash

26ndash

10

mdashmdash

mdashmdash

ndash11

3 ndash4

32

(32

3)(3

02)

(con

tinu

ed o

n ne

xt p

age)


- nullTue 14 Mar 2006 000925



Tab

le 2

(C

onti

nued

)

Mod

el I

Mod

el I

IM

odel

III

The

fta

Vio

lenc

ebT

heft

aV

iole

nceb

The

fta

Vio

lenc

eb

Uns

anct

ione

d O

ffen

ses

mdashmdash

Low

Off

endi

ngmdash

mdashmdash

mdash1

10

ndash22

1 (3

13)

(28

9)mdash

mdashM

ediu

m O

ffen

ding

mdashmdash

mdashmdash

ndash37

6 ndash8

49

(3

21)

(30

0)mdash

mdashH

igh

Off

endi

ngmdash

mdashmdash

mdashndash1

002

ndash10

65

(3

01)

(28

6)mdash

Peer

Vio

lenc

eT

heft

mdashmdash

mdashmdash

ndash79

6

ndash3

00

(1

21)

(10

3)P

seud

o R

20

10

003

0

18

006

0

26

011

u i

SD

301

8

28

42

18

21

21

55

16

43

21

04

ε iS

D41

45

378

6 45

40

403

3 44

99

400

3

Wal

d

211

563

310

7

206

49

74

53

32

748

138

05

(d

f)(1

0)(1

0)(1

6)(1

6)(2

3)(2

3)lo

gndashli

keli

hood

ndash126

314

4 ndash1

3001

83

ndash125

957

8 ndash1

2982

38

ndash125

453

5 ndash1

2953

77

Not

eS

D =

sta

ndar

d de

viat

ion

df

= d

egre

es o

f fr

eedo

m S

tand

ard

erro

rs a

ppea

r in

par

enth

eses

Mod

els

incl

ude

(non

-sig

nifi

cant

) ef

fect

s of

bla

ck H

ispa

nic

con

cent

rate

d di

sadv

an-

tage

pro

port

ion

blac

k c

rim

e ra

te 1

984

and

hig

h im

puls

ivit

ya

N =

32

77

bN

= 3

281

plt

05

plt

01

plt

001

(tw

o-ta

iled

)


- nullTue 14 Mar 2006 000925



Figure 2 Predicted Perceived Risk of Arrest by Experienced Certainty for (A) Theft and (B) Violence

A

0

10

20

30

40

50

60

00 gt00-05 06-25 26-100

Experienced Certainty

Per

ceiv

ed R

isk

of A

rres

t (T

heft

)

Without Controls With Controls

B

0

10

20

30

40

50

60

00 gt00-05 06-25 26-100


Per

ceiv

ed R

isk

of A

rres

t (V

iole

nce)



- nullTue 14 Mar 2006 000925

2ndash4 for both 2A and 2B) which graphs thepredicted values of perceived risk by experi-enced certainty These differences are statisti-cally significant a joint test rejects the nullhypothesis that the coefficients are equal (1= 2 = 3) for theft (2 = 1459 df = 2 p lt001) and violence (2 = 1027 df = 2 p lt001) As expected the coefficient for the low-est experienced certainty category (gt00ndash05)has the biggest negative effect those who havegotten away with their crimes perceive the cer-tainty of arrest for violence and theft as 15points lower than naiumlve youth (line 11) Relativeto naiumlve individuals those in the middle cate-gory (06ndash25) of experienced certainty per-ceive arrest certainty as 8 points lower for theftand 12 points lower for violence (line 12)Those in the highest experienced certainty cat-egory (26ndash10)mdashthe most frequently arrestedper crimemdashhave the smallest coefficient whichis not significantly different from naiumlve youth(line 13) Thus naiumlve individuals most close-ly resemble the group with the highest rate ofarrests per crime (compare bars 1 and 5 inFigures 2A and 2B) This overall pattern ofresults is consistent with those of Horney andMarshallrsquos (1992) study of convicted felonsand it supports our hypothesis of Bayesianlearning

We turn now to the effect of unsanctionedoffenses which are captured with three dummyvariables representing categories of offendingfrom low to high Our omitted category againis naiumlve individuals who have neither offendednor been arrested We again find evidence forupdating using a joint test we reject the nullhypothesis that net of prior perceived riskunsanctioned offenses have no effect on per-ceived risk for theft (2 = 1294 df = 3 p lt 01)and violence (2 = 1722 df = 3 p lt 001) Thuswe do not find support for the hypothesis thatyouth ignore new information and insteadanchor their estimates in initial values or thegamblerrsquos fallacy in which youth believe theyare ldquoduerdquo for an arrest We find support for theldquoshell of illusionrdquo hypothesis for violence Eachof the three coefficients for offending is nega-tive indicating that naiumlve individuals have high-er risk perceptions than unsanctioned offenders(lines 14ndash16 column 6 Table 2) For theft highand medium offending dummy variables arenegative However negative medium offend-

ing dummy variables and the positive lowoffending coefficient are not significant

We find support for the Bayesian learninghypothesis that on average as unsanctionedoffenses increase certainty of arrest declinesmonotonically For both theft and violenceour coefficients decrease (become more neg-ative) as unsanctioned offenses move from lowto high 1 gt 2 gt 3 (lines 14ndash16 Table 2) Forboth behaviors the differences among the cat-egories are statistically significant a joint testrejects the null hypothesis that the coefficientsare equal 1 = 2 = 3 for theft (2 = 916 df= 2 p lt 05) and violence (2 = 628 df = 2p lt 05) Compared to naiumlve individuals highoffenders (10 or more offenses) perceive therisk of arrest for violence about ten pointslower and medium offenders (3ndash9 offenses)about eight points lower Low offenders are twopoints lower but the effect is not significant(line 14) For theft high offenders are 10 pointslower than naiumlve youth (line 16) and mediumoffenders are four points lower than naiumlveyouth (line 15) but the latter coefficient is notsignificant Neither is the low offending coef-ficient Naiumlve youth most closely resemblelow unsanctioned offenders for both theft andviolence Again the overall pattern of resultsfor unsanctioned offenses is consistent with thehypothesis of Bayesian learning

We find support for the Bayesian learninghypothesis that delinquency by peers will beassociated with lower perceptions of certain-ty of arrest Net of personal experience vari-ables a unit change in our delinquent peermeasure is associated with an eight percentdecrease in perceived risk of arrest for theft anda three percent decrease for violence (line 17Table 2) Thus information from peers is notcompletely swamped by the more vivid per-sonal experiences with crime and arrestFinally we find that consistent with updatingprior perceived risk maintains a significanteffect on updated risk perceptions (line 10)even net of new information The coefficientsdiminish only slightly between Models II andIII

In sum the data support a Bayesian learn-ing view of formation of perceived arrest riskand Tittlersquos ldquoshell of illusionrdquo hypothesis Ofcourse evidence of such learning is impressivefor a rational choice model only if risk per-




- nullTue 14 Mar 2006 000925

ceptions are used in behavioral decisionsmdashtowhich we now turn

MMOODDEELLSS OOFF RRAATTIIOONNAALL CCHHOOIICCEETTHHEEFFTT AANNDD VVIIOOLLEENNCCEE

We specify a conceptual model of rationalchoice and criminal behavior diagrammed inFigure 3 The model controls for exogenousneighborhood variables and individual char-acteristics specifies theft or violence at timet as a function of prior theft or violence attime tndash1 and then introduces our lagged vari-ables capturing opportunity costs (grade pointaverage and employment) and our perceptualrational choice variables (risks rewards andopportunities) Perceived opportunity is meas-

ured at time t contemporaneously with ouroutcome of theft and violence in part becauseopportunities operate principally within thesituation15


NNEEGGAATTIIVVEE BBIINNOOMMIIAALL MMOODDEELLSS

Because our measures of theft and violenceare counts of offenses treating them as con-



Figure 3 Rational Choice Models of Violence and Theft

Individual

Age

Sex

Race

Family Structure

Impulsivity

Family Income t-1

Risk Preference t-1

Police Contact t-1

Rational Choice

Excitement t-1

Risk of Arrest t-1

Seen as Cool t-1

Employment t-1

Grades t-1

Opportunities t-1

Future Delinquency

Violence and Theft t

Prior Delinquency

Violence and Theft t-1

Neighborhood


Mobility

Percent Black

Crime Rate

15 This could create endogeneity between oppor-tunities and theft (violence)mdashas criminal acts couldalter perceptions of crime opportunitiesmdashresulting inan upwardly biased estimated effect This impliesthat our test of risks and rewards is conservative


- nullTue 14 Mar 2006 000925

tinuous and using linear models can yieldinconsistent and inefficient estimates of param-eters A better approach assumes the counts ofdelinquency are generated from a Poisson dis-tribution We estimated a Poisson regressionmodel and as expected found overdisper-sionmdashthe variance exceeded the mean countof delinquencymdashwhich if ignored can resultin inefficient estimates of coefficients anddownwardly biased standard errors (Long1997)16 Consequently we turned to a negativebinomial model which allows the rate of delin-quency to vary across individuals and there-fore the variance to exceed the mean Formallikelihood ratio tests show a better fit for thenegative binomial model for both theft (2 =13498 df = 1 p lt 001) and violence (2 =12082 df = 1 p lt 001) (for details see FigureS1 in our ASR Online Supplement)

In estimating our negative binomial modelwe found dependence in observations acrossindividuals which can result in inefficiencyand biased standard errors we therefore esti-mated a random-effects negative binomialmodel Formal likelihood ratio tests show thatthe random-effects model fit the data betterthan a pooled model (that ignores the depend-ence) for both theft (2 = 18752 df = 1 p lt001) and violence (2 = 17288 df = 1 p lt001) Therefore we present the random effectsnegative binomial specification estimated bymaximum likelihood (Hausman Hall andGirliches 1984) This model allows for overdis-persion in the Poisson model with the inclusionof a parameter and then layers a randomindividual effect onto the negative binomialmodel by assuming i (1 + i) follows a betadistribution with parameters a and b (see ASROnline Supplement) Unlike the random effectsPoisson model this model allows the rate tovary across individuals and time (even if thexits are constant)

Our negative binomial models like our Tobitmodels include lagged time-varying covariatesxitndash1 to ensure that the temporal specification

of our models is consistent with our theoreti-cal specif ication and include the laggedendogenous predictor (prior perceived cer-tainty) yitndash1 to control for stability or statedependence in theft and violence The randomeffects specification helps overcome potentialbias in the estimate of the effect of yitndash1 (andtherefore other estimates) by allowing forapproximately equal-correlated autocorrela-tion among disturbances (Hausman et al1984)

EESSTTIIMMAATTEESS OOFF PPAARRAAMMEETTEERRSS OOFF CCRRIIMMIINNAALL

BBEEHHAAVVIIOORR MMOODDEELLSS

Table 3 presents estimates of our random-effectsnegative binomial models of theft and violenceModel I (columns 1 and 2) presents coefficientsof our exogenous control variables As expect-ed we find that males and high impulsive indi-viduals engage in substantially more theft andviolence (lines 2 and 8) Older youth reportslightly more violence but not more theft (line3) In our sample compared to whites blacksengage in substantially more violence but notmore theft whereas Hispanics engage in slight-ly more of each (lines 4 and 5) Of our neigh-borhood variables only the 1984 crime rateaffects theft and violence (line 7) Among ourtime-varying lagged control variables biolog-ical parents exert a negative effect that is sig-nificant for theft but not violence (line 9) andincome is negatively associated with violencebut not theft (line 10) As expected net of ourbackground variables prior violence and theftexert strong lagged effects on future violenceand theft respectively (line 12) This highlightsthe importance of controlling for state depend-ence which helps control for unobserved het-erogeneity in our models17

Model II (columns 3 and 4 Table 3) adds ourrational choice variables including risk prefer-ence opportunity costs and perceived costsand rewards As hypothesized youth who liketo do daring things are substantially more like-ly to steal and fight (line 14) This is consistentwith our rational choice model which predictsthat risk-averse youth will avoid risky behaviors



16 Overdispersion can arise from a variety ofsources including unobserved heterogeneity (egomitted variables such as genetics early socializa-tion or structural ties) that affect individual propen-sity to commit crime clustering in the data (in ourcase neighborhoods and families) positive conta-gion and rate dependence (see McCullagh andNelder 1997 Barron 1992)

17 We are following previous research on deter-rence and crime by controlling for prior crime andarrest We find similar results when we control forexperienced certainty and unsanctioned offenses


- nullTue 14 Mar 2006 000925

like crime whereas risk-preferring youth will beattracted to crime Moreover it underscores theimportance of controlling for risk preferencewhen examining costs and rewards

We turn next to opportunity costs which aremeasured by grade point average and employ-ment Among current students higher grades aresignificantly associated with fewer acts of vio-lence and theft (line 15 Table 3) Thus highachieving students are unlikely to jeopardizetheir investments in schooling by committingcrimes Employment has no effect on theft butit has a positive effect on violence (line 16) Thelatter finding is inconsistent with an opportunitycosts mechanism but consistent with previous

research on delinquency Although the positiveeffect on violence persists when controlling forprior violence and being in school it may be dueto unobserved selection into jobs while a stu-dent We also find that youth who are not attend-ing schoolmdasheither because they graduated (themajority) or dropped outmdashand thus have nogrades commit fewer acts of violence and thefton average (line 17)

Turning to our crucial hypotheses concerningperceived costs and rewards we find support fora rational choice perspective In contrast to mostprevious studies of deterrence using panel sur-vey data we find a significant deterrent effectof arrest (perceived certainty weighted by per-



Table 3 Random Effects Negative Binomial Models of Violence and Theft (Waves 3ndash5)

Model I Model II Model III

Thefta Violenceb Thefta Violenceb Thefta Violenceb

VariablesmdashIntercept ndash163 ndash298 ndash127 ndash227 ndash162 ndash278

(041) (011) (048) (048) (048) (048)mdashFemale ndash058 ndash059 ndash045 ndash045 ndash041 ndash039

(011) (010) (011) (010) (011) (010)mdashAge ndash003 006 ndash004 002 ndash006 002

(002) (002) (002) (002) (002) (002)mdashBlack ndash011 061 ndash013 063 ndash020 049

(017) (017) (017) (017) (017) (017)mdashHispanic 022 021 021 024 022 023

(014) (015) (014) (015) (014) (015)mdashResidential Stability 014 010 019 013 018 011

(007) (007) (007) (007) (007) (007)mdashCrime Rate 1984 014 012 015 011 015 011

(006) (006) (006) (006) (006) (006)mdashHigh Impulsivity 028 026 025 029 023 028

(010) (010) (010) (011) (010) (010)Lagged Variables tndash1mdashBiological Parents ndash056 ndash014 ndash053 ndash014 ndash053 ndash015

(012) (012) (012) (012) (012) (012)mdashFamily Income ($1000rsquos) 000 ndash001 000 ndash001 000 ndash001

(000) (000) (000) (000) (000) (000)mdashParent(s) on Welfare 019 026 019 022 017 021

(013) (013) (013) (013) (013) (013)mdashPrior Behavior (Logged) 022 020 015 014 013 010

(006) (006) (006) (006) (006) (006)mdashPolice Contact 002 002 002 001 000 000

(002) (001) (002) (001) (002) (001)mdashRisk Preference mdash mdash 036 040 031 036

(011) (011) (011) (011)mdashGrades mdash mdash ndash014 ndash015 ndash010 ndash013

(005) (005) (005) (005)mdashEmployed mdash mdash 010 035 007 034

(010) (009) (010) (009)

(continued on next page)


- nullTue 14 Mar 2006 000925

ceived utility) (line 18 Table 3) Moreover thisholds not only for theft but also for violencesupporting McCarthyrsquos (2002) suggestion thatviolence is often instrumental Youth whobelieve they will be caught for their theft orviolence (and view arrest as bad) commit feweracts of theft or violence on average in the com-ing year

We also find support for the reward side ofthe rational choice process (eg Piliavin et al1986) First we find that the probability ofexcitement and kicks affects theft but not vio-lence (line 19 Table 3) Youth who expect excite-ment and kicks (and value excitement) fromstealing commit on average more acts of theftThis result combined with the null finding ofexcitement on violence supports a model ofsneaky thrills from theft (Katz 1988) From arational choice standpoint such thrills are psy-chic rewards from theft and are important indecisions to steal Second youth who expect tobe seen as cool (and value being seen as cool)if they commit theft or violence tend to commitsubstantially more acts of theft and violence onaverage in the coming year (line 20) This find-ing is consistent with ethnographic researchand subcultural theories that depict crime as an

alternate way of achieving status within refer-ence groups Moreover it suggests that a favor-able image from the standpoint of others is animportant psychic reward to crime (egMatsueda 1992)18

Model III (columns 5 and 6 Table 3) addscriminal opportunities to our models and revealssubstantial effects For both theft and violenceperceived opportunities to commit theft or vio-lence and get away with it substantially increasethe likelihood of theft and violence (line 21) Thecoefficient for each equation is identical in size(24) Recall that our variable perceived oppor-tunities to get away with theft or violence cap-tures an interaction between perceptions ofopportunities to commit crime and the certain-ty of arrest Thus including it alters the mean-ing of our perceived risk effects which are nowapproximately main effects in an interactionmodel This finding supports a routine activitiesor criminal opportunities approach interactingwith a perceptual rational choice model



Table 3 (Continued)



Lagged Variables tndash1mdashNot in School mdash mdash ndash126 ndash068 ndash108 ndash069

(038) (030) (038) (030)mdashPerceived Risk of Arrest mdash mdash ndash008 ndash010 ndash008 ndash009

(003) (003) (003) (003)mdashPerceived Excitement mdash mdash 019 002 019 001

(006) (004) (006) (004)mdashPerceived Coolness mdash mdash 016 016 018 014

(004) (004) (004) (004)Variables at Time tmdashPerceived Opportunities mdash mdash mdash mdash 024 024

(003) (003)

Wald 2 14389 17015 22809 27408 29301 35462(df) (16) (16) (23) (23) (24) (24)log-likelihood ndash263317 ndash279107 ndash259608 ndash274600 ndash256692 ndash271607

Note Standard errors appear in parentheses Models include (non-significant) effects of no siblings concentrateddisadvantage and proportion blacka N = 3297 b N = 3298 p lt 05 p lt 01 p lt 001 (two-tailed)

18 The estimates of rational choice coefficientsare robust to controls for other variables such asdelinquent peers


- nullTue 14 Mar 2006 000925

Finally we can examine the hypothesisderived from a limited rationality model thatcriminals discount consequences with a longertime horizon such as formal sanction whilefocusing on immediate criminal rewards andopportunities Standardized coefficients showthe relative effects of perceived cost and rewardsto be comparable in magnitude19 For theft thestandardized coefficients for excitement (15)certainty (ndash13) and coolness (18) are similarin absolute value For violence the correspon-ding coefficients for certainty (ndash14) and cool-ness (17) are similar while excitement has noeffect Thus we fail to find support for the dis-counting hypothesis and instead we find sup-port for both sides of a rational choicemodelmdashcosts and returns

DDIISSCCUUSSSSIIOONN

Our results support a Bayesian learning modelof perceived risk formation a rational choicemodel of criminal behavior and a deterrencehypothesis of perceived risk of arrest Previousresearch on deterrence using surveys of per-ceived risk has yielded disparate findingsCross-sectional studies of self-reported crimefound small deterrent effects but these studieswere criticized because the temporal orderingof crime and perceived certainty contradictedtheir causal ordering Panel studies using laggedeffects to resolve this problem generally foundno deterrent effects Our results contrast sharplywith these prior panel studies We use (1) morerefined measures of certainty of arrest based ona probability scale (2) a large sample of youthin high risk neighborhoods that insures ade-quate variation in the modelsrsquo variables and(3) appropriate random effects negative bino-mial estimators that overcome bias and ineffi-ciency Interestingly cross-sectional vignettestudies which use criminal intentions as theoutcome variable (to address issues of causalorder) find consistent deterrent effects of per-ceived risk While it may be tempting to dismissthe vignette results because of a potentialdemand effectmdashrespondents who rate a crime

scenario as certain of arrest probably under-state their intentions to commit the crimemdashourpanel models of self-reports provide similarresults Future research on perceived risk usingboth self-report outcomes and behavioral inten-tions is needed to unravel these processes

Our results are robust to violations of statis-tical assumptions in our models (see ASR OnlineSupplement) Nevertheless at least four caveatsare in order First the DYS study sampled highrisk disorganized neighborhoods and we havebeen careful not to generalize to low risk neigh-borhoods We can speculate however that if ourmodels can be generalized such neighborhoodshave lower rates of delinquency in part becausethe young residents have fewer delinquent peersthink arrest will follow crime due to the shell ofillusion and do not see crime as a way of gain-ing excitement or being cool Second althoughwe discuss cognitive heuristics to illustrate howlearning risk perceptions might depart fromBayesian learning we have not formally testedfor heuristics This would require calibratingour respondentsrsquo estimates against a true stan-dard so we could test for objective bias in updat-ing estimates Indeed our estimates are notsufficiently calibrated to rule out cognitiveheuristics in our risk estimatesmdashwe can onlyrule out the extreme hypothesis that they elim-inate a Bayesian form of updating entirely Thirdalthough our models use strong measures ofcosts and returns to crime based on ethnographicresearch they have not exhausted all possibleincentives For example we have controlled forthe monetary return to crime by specifying ahypothetical amount in our rewards and costsmeasures but have not examined variation inmonetary returns Fourth we have relied on aone-year lagged effect of perceived costs andreturns to crime which may have understatedtheir effects The pertinent incentives are thoseperceived at the time a criminal opportunitypresents itself and these may have changedsince the measurement in the previous wave(Piliavin et al 1986)

Our principal finding that criminal acts ofviolence and theft conform to a rational choicemodel may surprise sociologists skeptical ofrational choice theories for being reductionistfor being relatively simple models laden withheavy assumptions and for threatening to usurpthe traditional domain of the discipline We sug-gest that rational choice and institutional theo-



19 Our standardized negative binomial coefficientsare exp(ksk) ndash 1 where k is the k-th coefficient andsk is the standard deviation of the k-th independentvariable (Long 1997)


- nullTue 14 Mar 2006 000925

ries can be complementary In fact in mostinstitutional analyses it may be prudent to beginwith an individual-level rational actor model asa parsimonious point of departure before iden-tifying the limitations of such a model and mov-ing to complicate matters with more nuancedconcepts of structure preference and organi-zation (eg Weber [19211922] 1978) Ourresults while based on an individual-level modelof action do not eschew the role of broaderinstitutions and groups in decision making Wefind that the educational institution by incul-cating commitments among students providesan important opportunity cost for offendingMoreover we find the legal institution with itsdispassionate threats of punitive sanctions to bean intimate part of criminal decisions throughyouthsrsquo perceptions of the risk of sanctionPerceived risk in turn is formed in part byinformation gleaned from informal peer groupsas well as direct experience with the legal sys-tem We also find that social status withingroups as indicated by the importance of beingseen as cool is a key component of decisionsto offend Thus institutional arrangementsunderlie the parameters of our rational choicemodel

Finally our results have some indirect impli-cations for public policy Although we find a sig-nificant deterrent effect of perceived risk oncrime the effect is modest in size In a model(not reported) that disentangles perceived riskof arrest from the value (severity) of arrest andexcludes opportunities we find that a ten-pointincrease in perceived risk (on a 0ndash100 proba-bility scale in which mean risk is 465 for theftand 436 for violence) is associated with a threepercent reduction in the expected number ofthefts and a five percent reduction in the expect-ed number of violent acts20 The correspondingeffects for rewards are larger A ten-pointdecrease in the probability of ldquobeing seen ascoolrdquo is associated with a six percent reductionin theft and a seven percent reduction in vio-lence For excitement the effect is a six percentreduction (for theft) Our finding that perceived

risk of punishment has a small but significanteffect is consistent with rational choice (andsocial learning) models of individual behaviorthe utilitarian assumptions underlying our crim-inal justice system and therefore the legitima-cy of using some minimal level of the threat ofpunishment imposed by the state

But given that the United States has one ofthe highest arrest and imprisonment rates amongWestern nations increasing the perceived prob-ability of arrest by 01 would likely involve dra-conian steps by the criminal justice systemThis implies that policies for increasing theobjective probability of punishment by them-selves may be of limited value for reducingthe crime rate substantially (Cullen et al 2002)Indeed the modest explained variance in ourmodels of theft (seven percent) and violence(nine percent) suggests that other processessuch as socialization and embeddedness in insti-tutions may dwarf the effects of punishment andsuggest more efficient policy interventions21

Our results suggest equally promisingavenues for intervention school programs toteach peer groups that crime is not cool insti-tutional programs to increase opportunity coststo crime by enhancing human capital and recre-ational programs to provide outlets for youthwith strong preferences for risk and excitement(for promising leads see Elliott Hamburg andWilliams 1998) Furthermore our finding thatdelinquents learn they can get away with crime(and each undetected offense further reducestheir estimates of the risk of arrest) underscoresthe importance of early interventions occur-ring before delinquent careers develop and riskestimates decline Perhaps educational programsteaching youth that the actual long run risk ofdetection over a criminal career is actually highmay be worth pursuing

Given the modest effects of the perceivedthreat of punishment it is troubling that ldquogettough on crimerdquo policies such as sentencingguidelines ldquothree strikes and yoursquore outrdquo lawsand mandatory minimum sentences have gainedsuch traction at the outset of the 21st century



20 Note that our sample of 1472 individuals com-mitted 3321 acts of violence and 3307 acts of theft(per year) Therefore increasing the probability ofarrest by 01 would be expected to ldquosaverdquo 156 actsof violence and 109 acts of theft in a year

21 We are using a pseudo-R2 based on the likeli-hood ratio test statistic R2 = 1 ndash (LL)2n where L

is the maximum of the likelihood function for anintercept-only null model and L is that for thehypothesized model (see Long 1997)


- nullTue 14 Mar 2006 000925

This is particularly ironic given that Beccariarsquos([1764] 1963) proposed system of justice basedon utilitarian ideas emphasized curtailing theseverity of punishments and using them only asa last resort

Sociological analyses suggest that the ration-ale for punishment has less to do with deterrenceand more to do with larger political processesendemic to US capitalism particularly in latemodernity For example Garland (2001) com-pares the United States to England and despairsthat in late modernity a culture of risk man-agement (backed by economic rationality) cen-tralized state power (which has shifted decisionmaking away from criminal justice practition-ers to politicians) and a shift from welfare(rehabilitation and prevention) to penal orien-tations has led to policies of severe punishmentincreasing surveillance and determinate sen-tencing Garlandrsquos conclusions about late moder-nity rest on commonalities betweencontemporary United States and England Incontrast Whitman (2003) holds constant latemodernity by contrasting contemporary UnitedStates with France and Germany He concludesthat the confluence of a strong state (in whichbureaucrats are free to interpretmdashsoftenmdashthelaw to fit individual circumstances) (see Rosettand Cressey 1976) and memories of the brutalpunishments doled out to the lower classes dur-ing the ldquoreign of terrorrdquo led to mild punish-ments (mercy) for all offenders in France andGermany This ldquoleveling uprdquo of treatment toapproximate that of aristocrats is absent in theUnited States because of its weak state andbecause US citizens lack a shared history ofbrutality The result is a shift from the judicialto the legislative whereby mechanical punish-ments are subject to political processes in whichparties clamor to appear tough on crime

Policies of getting tough on crime alwaysresonate well in the US political arenaHowever such resonance is often based more onideology than empirical research on punitivepractices such as evaluations of CaliforniarsquosThree Strikes laws that show at best small gen-eral deterrent effects (eg Zimring Hawkins

and Kamin 2001) and our findings of modestdeterrent effects of perceived certainty of arrestThe finding of some deterrent effect howevermodest is not surprising given the need forthe appearance of legitimacy in the legal systemto maintain the consent of the governed Whatis surprising however is the persistence ofincreasingly harsh punitive crime policies inthe United States in the face of this growingbody of research

Ross L Matsueda is Professor of Sociology at theUniversity of Washington and is a Visiting Scholar atthe University of Wisconsin-Madison His researchinterests center on the sociology of crime law anddeviance social psychology and methods His cur-rent projects include the Seattle Neighborhoods andCrime Project (with Robert Crutchfield Avery Guestand Kevin Drakulich) which is a study of neighbor-hood organization codes of the street and spatialdynamics of violence in Seattle neighborhoods LifeCourse Trajectories of Drug Use and Crime (withKreager Huizinga and Elena Erosheva) and siblingmodels of child behavior problems (with LingxinHao)

Derek A Kreager is a PhD candidate in theDepartment of Sociology at the University ofWashington His research interests include crimethe life course deterrence and social network analy-sis His dissertation explores the links between peernetworks and adolescent violence His recent workincludes ldquoStrangers in the Halls Isolation andDelinquency in School Networksrdquo Social Forces83(2004)351ndash90

David Huizinga is Senior Research Associate in theInstitute for Behavioral Science at the University ofColorado He is principal investigator of the DenverYouth Survey now in its 19th year and has been co-principal and principal investigator of the NationalYouth Survey now in its 29th year Both are longitu-dinal studies of problem and successful behaviorsover the life course that focus on delinquency vio-lence drug use victimization and mental healthHe has also recently been the principal investigatorof two comparative studies (with Karl Schumann)investigating the effect of the transition from schoolto work and the effect of juvenile justice processingon subsequent criminal behavior in Germany and theUnited States




- nullTue 14 Mar 2006 000925



AAPPPPEENNDDIIXX

Table A1mdashConcepts and Measures

Variables

Background and Contextual VariablesmdashFemalemdashAgemdashBlackmdashHispanicmdashNo SiblingsmdashConcentrated Disadvantage

mdashProportion BlackmdashResidential Stability

mdashCrime Rate 1984

mdashHigh Impulsivity

mdashBiological Parentstndash1

mdashParent(s) on Welfaretndash1

mdashFamily Incometndash1

mdashMissing Parent Reporttndash1

Explanatory VariablesmdashExperienced Certaintyt

mdashUnsanctioned Offensest

mdashPeer Theftt

mdashPeer Violencet

mdashPolice Contacttndash1

mdashRisk Preferencetndash1

mdashGradestndash1

mdashNot in Schooltndash1

mdashEmployedtndash1

mdashTheft (Violence) Excitement tndash1

mdashTheft (Violence) Coolnesstndash1

mdashTheft (Violence) Perceived Risk mdashmdashof Arresttndash1

mdashTheft (Violence) Opportunitiest

mdashPrior Theft (Violence)tndash1

Dependent VariablesmdashTheft (Violence) Perceived Risk of mdashmdashArresttndash1

mdashViolencet

mdashTheftt

Description

XDummy variable for sexAge of respondent in 1988Dummy variable for blacksDummy variable for Hispanic originDummy variable for respondents without siblingsSum of percent poverty percent unemployed percent female-headed

households by census block groupPercent black by census block groupSum of percent homeowners and percent in same household by census

block groupTotal number of crimes reported to police in 1984 per ten residents by

neighborhoodParent strongly agrees with 1988 items (1) Canrsquot sit still restless or

hyperactive (2) Impulsive or acts without thinking (3) Wants to havethings right away (4) impatient ( = 72)

Dummy variable indicating living with both biological parentsDummy variable for welfare receipt reported by parentFamily income reported by parent in $1000rsquosDummy variable for missing on income or welfare (no parent interview)

Ratio of number of times questioned or arrested by police to self-reportedcrimes cumulative across waves and categorized into three dummyvariables with non-offending the omitted category

Three dummy variables indicating levels of self-report offending foroffenders who have not been questioned or arrested by the police

Number of friends in the past year who have (1) stolen something worthless than $5 (2) stolen something between $5ndash$100 (3) stolen some-thing worth more than $100 ( = 82)

Number of friends in the past year who have (1) gotten into a fight withschoolmates or friends (2) hit someone with the idea of hurting them(3) attacked someone with a weapon or with the idea of seriouslyhurting them ( = 84)

Sum of self-reported counts (1) arrested or (2) questioned by policeDo you agree with the statement ldquoI like to do daring thingsrdquoSelf-reported grade-point averageMissing grades due to not being in schoolDummy variable indicating self-reported employment during the past yearPerceived probability of excitement for committing theft (violence)

(0ndash100 10 point increments) weighted by how good or bad that wouldbe (5-point scale)

Perceived probability of being seen as cool for committing theft (vio-lence) weighted by how good or bad that would be

Perceived probability of being picked up by the police for committingtheft (violence) weighted by how good or bad that would be

How often do you have a chance to commit theft (violence) and get awaywith it (Five-point scale)

Logged sum of prior theft and violence counts (see below)

Perceived probability of being picked up by police for theft (violence)

Sum of self-reported violence counts (1) aggravated assault (2) simpleassault (3) gang fights

Sum of self-reported theft counts (1) stolen less than $5 (2) stolenbetween $5 and $100 (3) stolen over $100 (4) shoplifting (5) pursesnatching (6) auto larceny (7) fencing


- nullTue 14 Mar 2006 000925

RREEFFEERREENNCCEESS

Achen Christopher H 1992 ldquoSocial PsychologyDemographic Variables and Linear RegressionBreaking the Iron Triangle in Voting ResearchrdquoPolitical Behavior 14195ndash211

Akers Ronald L 1998 Social Learning and SocialStructure Boston MA Northeastern University

Andenaes Johannes 1975 ldquoGeneral PreventionRevisited Research and Policy ImplicationsrdquoJournal of Criminal Law and Criminology66338ndash65

Anderson Elijah 1999 Code of the Street DecencyViolence and the Moral Life of the Inner CityNew York W W Norton

Barron David N 1992 ldquoThe Analysis of CountData Overdispersion and Autocorrelationrdquo Pp179ndash220 in Sociological Methodology vol 22edited by P V Marsden Cambridge MABlackwell Publishers

Beccaria Cesare [1764] 1963 On Crimes andPunishments Indianapolis IN Bobbs-Merrill

Becker Gary S 1968 ldquoCrime and Punishment AnEconomic Approachrdquo Journal of PoliticalEconomy 76169ndash217

Bentham Jeremy [1789] 1948 An Introduction to thePrinciple of Morals and Legislations Oxford UKBlackwell

Blackwell Brenda Sims and Sarah Eschholz 2002ldquoSex Differences in Rational Choice TraditionalTests and New Directionsrdquo Pp 109ndash36 in RationalChoice and Criminal Behavior Recent Researchand Future Challenges edited by A R Piqueroand S G Tibbetts New York Routledge

Carroll J S 1978 ldquoA Psychological Approach toDeterrence The Evaluation of CrimeOpportunitiesrdquo Journal of Personality and SocialPsychology 361512ndash20

Chambliss William J 1967 ldquoTypes of Deviance andthe Effectiveness of Legal Sanctionsrdquo WisconsinLaw Review Summer703ndash19

Clarke Ronald V and Derek B Cornish 1985ldquoModeling Offendersrsquo Decisions A Frameworkfor Research and Policyrdquo Pp 147ndash85 in Crime andJustice An Annual Review of Research Volume 6edited by N Morris and M Tonry Chicago ILUniversity of Chicago Press

Cloward Richard A and Lloyd E Ohlin 1960Delinquency and Opportunity New York FreePress

Cohen Albert K 1955 Delinquent Boys New YorkFree Press

Cohen Lawrence E and Marcus Felson 1979ldquoSocial Change and Crime Rate Trends A RoutineActivity Approachrdquo American Sociological Review44588ndash608

Coleman James S 1990 Foundations of SocialTheory Cambridge MA Harvard

Cook Philip 1980 ldquoResearch in Criminal

Deterrence Laying the Groundwork for the SecondDecaderdquo Pp 211ndash68 in Crime and Justice AnAnnual Review of Research vol 2 edited by NMorris and M Tonry Chicago IL University ofChicago Press

Cragg John G 1971 ldquoSome Statistical Models forLimited Dependent Variables with Application tothe Demand for Durable Goodsrdquo Econometrica39829ndash44

Cullen Francis T Travis C Pratt Sharon LevranMiceli and Melissa M Moon 2002 ldquoDangerousLiaison Rational Choice Theory as the Basis forCorrectional Interventionrdquo Pp 279ndash97 in RationalChoice and Criminal Behavior edited by A RPiquero and S G Tibbetts New York Routledge

Douglas Mary 1986 How Institutions ThinkSyracuse NY Syracuse University Press

Ehrlich Isaac 1973 ldquoParticipation in IllegitimateActivities A Theoretical and EmpiricalInvestigationrdquo Journal of Political Economy81521ndash65

Elliott Delbert S Beatrix Hamburg and Kirk RWilliams eds 1998 Violence in American SchoolsNew York Cambridge University Press

Esbensen Finn-Aage and David Huizinga 1990ldquoCommunity Structure and Drug Use From aSocial Disorganization Perspectiverdquo JusticeQuarterly 7691ndash709

Frazier Charles E and Thomas Meisenhelder 1985ldquoExploratory Notes on Criminality and EmotionalAmbivalencerdquo Qualitative Sociology 8266ndash284

Garland David 1990 Punishment and ModernSociety Chicago IL University of Chicago

mdashmdashmdash 2001 The Culture of Control Chicago ILUniversity of Chicago

Greene William H 2002 LIMDEP Version 80Reference Guide Plainview NY EconometricSoftware Inc

mdashmdashmdash 2003 Econometric Analysis Upper SaddleRiver NJ Prentice Hall

Gottfredson Michael R and Travis Hirschi 1990 AGeneral Theory of Crime Stanford CA StanfordUniversity Press

Hagan John and Bill McCarthy 1997 Mean StreetsYouth Crime and Homelessness New YorkCambridge University Press

Hagan John John Simpson and A R Gillis 1987ldquoClass in the Household A Power-Control Theoryof Gender and Delinquencyrdquo American Journal ofSociology 92788ndash816

Hausman Jerry Bronwyn Hall and Zvi Griliches1984 ldquoEconometric Models for Count Data withan Application to the PatentsmdashR amp DRelationshiprdquo Econometrica 52909ndash38

Hechter Michael and Satoshi Kanazawa 1997ldquoSociological Rational Choice Theoryrdquo AnnualReview of Sociology 23191ndash214

Heimer Carol A 1988 ldquoSocial-Structure




- nullTue 14 Mar 2006 000925

Psychology and the Estimation of Riskrdquo AnnualReview of Sociology 14491ndash519

Hirschi Travis 1969 Causes of DelinquencyBerkeley CA University of California Press

Horney Julie and Ineke H Marshall 1992 ldquoRiskPerceptions Among Serious Offenders The Roleof Crime and Punishmentrdquo Criminology30575ndash94

Horowitz Ruth 1983 Honor and the AmericanDream New Brunswick NJ Rutgers UniversityPress

Hume David [1739] 2000 A Treatise of HumanNature edited by David Fate Norton and Mary JNorton Oxford UK Oxford University Press

Jacob Herbert 1979 ldquoRationality and CriminalityrdquoSocial Science Quarterly 59584ndash5

Jacobs Bruce A 2000 Robbing Drug Dealers NewYork Aldine

Jenkins Patricia H 1995 ldquoSchool Delinquency andSchool Commitmentrdquo Sociology of Education68(3)221ndash39

Jensen Gary 1969 ldquoCrime Doesnrsquot Pay Correlatesof a Shared Misunderstandingrdquo Social Problems17189ndash201

Kahneman Daniel and Amos Tversky 1972ldquoSubjective Probability A Judgment ofRepresentativenessrdquo Cognitive Psychology3430ndash54

Kahneman Daniel Peter P Wakker and RakeshSarin 1997 ldquoBack to Bentham Explorations ofExperienced Utilityrdquo The Quarterly Journal ofEconomics 112375ndash405

Katz Jack 1988 Seductions of Crime Moral andSensual Attractions in Doing Evil New York BasicBooks Inc

Klepper Steven and Daniel S Nagin 1989 ldquoTheDeterrent Effect of Perceived Certainty andSeverity of Punishment Revisitedrdquo Criminology27721ndash46

Langley John producer [1988] 2005 CopsTelevision series Los Angeles CA FoxBroadcasting Company

Levitt Steven 1997 ldquoUsing Electoral Cycles inPolice Hiring to Estimate the Effect of Police onCrimerdquo American Economic Review 87270ndash90

Lochner Lance 2005 ldquoIndividual Perceptions ofthe Criminal Justice Systemrdquo Working Paper No9474 National Bureau of Economic ResearchCambridge MA

Long Scott 1997 Regression Models for Categoricaland Limited Dependent Variables Thousand OaksCA Sage

Maddala G S 1983 Limited-Dependent andQualitative Variables in Econometrics CambridgeUK Cambridge University Press

Maestro Marcello T 1973 Cesare Beccaria and theOrigins of Penal Reform Philadelphia PA TempleUniversity

Marvell Thomas and Carlisle Moody 1994 ldquoPrison

Population Growth and Crime Reductionrdquo Journalof Quantitative Criminology 10109ndash40

Matsueda Ross L 1992 ldquoReflected AppraisalsParental Labeling and Delinquent BehaviorSpecifying a Symbolic Interactionist TheoryrdquoAmerican Journal of Sociology 971577ndash1611

McCall Nathan 1994 Makes Me Wanna HollerNew York Vintage

McCarthy Bill 2002 ldquoNew Economics ofSociological Criminologyrdquo Annual Review ofSociology 28417ndash42

McCarthy Bill and John Hagan 2005 ldquoDangerDeterrence and the Decision to Offendrdquo SocialForces 831065ndash96

McCullagh P and J A Nelder 1997 GeneralizedLinear Models 2d ed London UK Chapman ampHall

McFadden Daniel L and Kenneth E Train 1996ldquoConsumersrsquo Evaluation of New ProductsLearning from Self and Othersrdquo Journal ofPolitical Economy 104683ndash703

Morrow James D 1994 Game Theory for PoliticalScientists Princeton NJ Princeton University

Nagin Daniel S 1978 ldquoGeneral Deterrence AReview of the Empirical Evidencerdquo Pp 95ndash139 inDeterrence and Incapacitation Estimating theEffects of Criminal Sanction on Crime Rates edit-ed by A Blumstein J Cohen and D NaginWashington DC National Academy Press

mdashmdashmdash 1998 ldquoCriminal Deterrence Research at theOutset of the Twenty-First Centuryrdquo Crime andJustice A Review of Research 231ndash42

Nagin Daniel S and Raymond Paternoster 1994ldquoPersonal Capital and Social Control TheDeterrence Implications of Individual Differencesin Criminal Offendingrdquo Criminology 32581ndash606

Nagin Daniel S and Greg Pogarsky 2001ldquoIntegrating Celerity Impulsivity and ExtralegalSanction Threats into a Model of GeneralDeterrence Theory and Evidencerdquo Criminology39865ndash92

Parker Jerry and Harold G Grasmick 1979ldquoLinking Actual and Perceived Certainty ofPunishment An Exploratory Study of an UntestedProposition in Deterrence Theoryrdquo Criminology17365ndash79

Pashukanis Evgeny B [1929] 1978 Law andMarxism A General Theory edited by ChrisArthur London UK Ink Links

Paternoster Raymond 1987 ldquoThe Deterrence Effectof the Perceived Certainty and Severity ofPunishment A Review of the Evidence andIssuesrdquo Justice Quarterly 4173ndash217

Paternoster Raymond Shawn Bushway RobertBrame and Robert Apel 2003 ldquoThe Effect ofTeenage Employment on Delinquency andProblem Behaviorsrdquo Social Forces 82297ndash335

Piliavin Irving Rosemary Gartner Craig Thorntonand Ross L Matsueda 1986 ldquoCrime Deterrence




- nullTue 14 Mar 2006 000925

and Rational Choicerdquo American SociologicalReview 51101ndash19

Pinderhughes Howard 1997 Race in the HoodConflict and Violence Among Urban YouthMinneapolis University of Minnesota

Piquero Alex R and Greg Pogarsky 2002 ldquoBeyondStafford and Warrrsquos Reconceptualization ofDeterrence Personal and Vicarious ExperiencesImpulsivity and Offending Behaviorrdquo Journal ofResearch in Crime and Delinquency 39153ndash86

Ploeger Matthew 1997 ldquoYouth Employment andDelinquency Reconsidering a ProblematicRelationshiprdquo Criminology 35659ndash75

Pogarsky Greg and Alex R Piquero 2003 ldquoCanPunishment Encourage Offending Investigatingthe ldquoResettingrdquo Effectrdquo Journal of Research inCrime and Delinquency 40(1)95ndash120

Pogarsky Greg Alex R Piquero and RaymondPaternoster 2004 ldquoModeling Change inPerceptions about Sanction Threats The NeglectedLinkage in Deterrence Theoryrdquo Journal ofQuantitative Criminology 20343ndash69

Posner Richard A 1998 Economic Analysis of Law5th ed Boston MA Aspen Law amp Business

Raftery Adrian E 2001 ldquoStatistics in Sociology1950ndash2000 A Selective Reviewrdquo SociologicalMethodology 311ndash46

Rosett Arthur and Donald R Cressey 1976 Justiceby Consent Philadelphia PA Lippincott

Sampson Robert J and Jacqueline Cohen 1988ldquoDeterrent Effects of Police on Crime AReplication and Theoretical Extensionrdquo Law andSociety Review 22163ndash89

Sampson Robert J and John H Laub 1993 Crimein the Making Pathways and Turning PointsThrough Life Cambridge MA Harvard UniversityPress

Short James F Jr and Fred L Strodtbeck 1965Group Process and Gang Delinquency ChicagoIL University of Chicago

Staff Jeremy and Christopher Uggen 2003 ldquoTheFruits of Good Work Early Work Experiencesand Adolescent Deviancerdquo Journal of Research inCrime and Delinquency 40263ndash90

Stafford Mark C and Mark Warr 1993 ldquoAReconceptualization of General and SpecificDeterrencerdquo Journal of Research in Crime andDelinquency 30123ndash35

Stewart Eric A 2003 ldquoSchool Social Bonds SchoolClimate and School Misbehavior A MultilevelAnalysisrdquo Justice Quarterly 20(3)575ndash604

Steinberg Laurence Suzanne Fegley and Sanford MDornbusch 1993 ldquoNegative Impact of Part-Time

Work on Adolescent Adjustment Evidence froma Longitudinal-Studyrdquo Developmental Psychology29171ndash80

Sullivan Mercer L 1989 ldquoGetting Paidrdquo YouthCrime and Work in the Inner City Ithaca NYCornell University Press

Sunstein Cass R 1999 ldquoBehavioral Law andEconomics A Progress Reportrdquo American Lawand Economics Review 1115ndash57

Tallman Irving and Louis N Gray 1990 ldquoChoicesDecisions and Problem-Solvingrdquo Annual Reviewof Sociology 16405ndash33

Tittle Charles R 1980 Sanctions and SocialDeviance The Question of Deterrence New YorkPraeger

Tunnell Kenneth D 1992 Choosing Crime TheCriminal Calculus of Property Offenders ChicagoIL Nelson-Hall

Tversky Amos and Daniel Kahneman 1974ldquoJudgment Under Uncertainty Heuristics andBiasesrdquo Science 1851124ndash31

Weber Max [19211922] 1978 Economy andSociety edited by G Roth and C WittichBerkeley CA University of California Press

Whitman James Q 2003 Harsh Justice OxfordUK Oxford University

Williams Kirk R and Richard Hawkins 1986ldquoPerceptual Research on General Deterrence ACritical Reviewrdquo Law and Society Review20545ndash72

Wilson James Q and Barbara Boland 1978 ldquoTheEffect of the Police on Crimerdquo Law and SocietyReview 12367ndash90

Wilson James Q and Richard J Herrnstein 1985Crime and Human Nature The Definitive Study ofthe Causes of Crime New York Simon andSchuster

Winterfeldt Detlof Von and Ward Edwards 1986Decision Analysis and Behavioral ResearchCambridge UK Cambridge University Press

Wooldridge Jeffrey 2002 Econometric Analysis ofCross Section and Panel Data Cambridge MAMIT Press

Wright John Paul Francis T Cullen and NicolasWilliams 1997 ldquoWorking While in School andDelinquent Involvement Implications for SocialPolicyrdquo Crime and Delinquency 43203ndash21

Zimring Franklin E and Gordon J Hawkins 1973Deterrence Chicago IL University of Chicago

Zimring Franklin E Gordon J Hawkins and SamKamin 2001 Punishment and Democracy NewYork Oxford University




- nullTue 14 Mar 2006 000925

and uneducated and their behaviors as beyondthe reach of formal sanctions (eg Gottfredsonand Hirschi 1990) Thus a finding that streetcrimes follow rational choice principles wouldprovide strong evidence for rational choice per-spectives

Crime is an important arena for investigatingrational choice for another reason Utilitarianprinciples and their accompanying psycholog-ical assumptions undergird our legal institution(eg Maestro 1973) This connection is rootedin writings of the classical school Long agoBentham ([1789] 1948) argued that happinessis a composite of maximum pleasure and min-imum pain and that the utilitarian principlemdashthe greatest happiness for the greatestnumbermdashunderlies morals and legislation ForBentham punishment by the state constitutesone of four sanctionsmdashpolitical moral physi-cal and religiousmdashthat shape pleasures andpains Beccaria ([1764] 1963) influenced by themoral philosophers of the Enlightenmentassumed that criminal laws reflect the terms ofa social contract in which members of societyreceive protection of their rights to personalwelfare and private property in exchange forrelinquishing the freedom to violate the rightsof others Those rights are protected by the statethrough deterrence threatening potential trans-gressors with just enough punishment to out-weigh the pleasures of crime1 Beccariaattempted to reform the unjust and brutal legalsystem of eighteenth-century Europe by devel-oping a rational system that specified laws clear-ly and a priori (so individuals have fullinformation about the consequences of theiracts) eliminated judicial discretion (so all cit-izens are equal in the eyes of the law) and madepunishments certain swift and no more severethen needed to deter the public from crime

Critical legal scholars however challengethis view of the legal system They see the sys-tem as an ideological justification (manifestfunction) of modern capitalist societies whichdisguises its true essence (latent function) aslegitimizing inequitable class relations For

example Pashukanis ([1929] 1978169) devel-ops a Marxist commodity exchange theory oflaw whereby under capitalism punishmentbecomes an exchange ldquoin which the offenderlsquopays his debtrsquo and wherein crime becomes lsquoaninvoluntarily concluded contractrsquordquo (Garland1990113) For Garland (1990117) ldquothe key tounderstanding criminal law in class terms is toappreciate the ways in which particular interestsare interwoven with general onesrdquo such thatprotection of class interests are disguised asprotection of universal interests As for deter-rence he argues that ldquothe simple fact is that nomethod of punishment has ever achieved highrates of reform or of crime controlmdashand nomethod ever willrdquo (Garland 1990228) Ourpurpose is not to adjudicate between views ofthe legal system as a mechanism for achievingthe greatest happiness for the greatest numberas Bentham ([1789] 1948) and Beccaria ([1764]1963) argue or as a tool for exercising ldquoclassterrorrdquo and mystifying class relations asPashukanis and Garland argue Rather ourobjective is to examine the question of ration-al choice and deterrence which cuts to the coreof assumptions underlying our legal institutionDo individuals act as if they are rational andrespond to incentives as rational choice andclassical theorists assume Or does conformi-ty lie principally outside the purview of pun-ishment and rational choice as Garland andother sociologists of law maintain

Recent treatments of rational choice anddeterrence follow Beckerrsquos (1968) seminal arti-cle outlining an expected utility model of crim-inal decision making (see McCarthy 2002)Beckerrsquos model specifies that individuals willengage in crime when expected utility fromcommitting crime is greater than expected util-ity from not committing crime The utility func-tion in turn is a function of two additivecomponents (1) the utility of returns from crime(the ldquobootyrdquo) weighted by the objective proba-bility of getting away with it plus (2) the util-ity of returns minus the punishment eachweighted by the objective probability of gettingcaught and punished

Early empirical studies of Beckerrsquos modeluse statistical models of aggregate crime ratesand focus on deterrent effects of risk of impris-onment (measured by imprisonment per capi-ta) or risk of arrest (measured by arrests perreported crimes) Ehrlich (1973) found deterrent



1 The form of US criminal laws reflects utility anddeterrence theories rather than moralizing aboutillegal acts the statutes stipulate simply and dis-passionately that whosoever engages in a certaincrime will be punished with a certain sanction


- nullTue 14 Mar 2006 000925













- nullTue 14 Mar 2006 000925







SSAANNCCTTIIOONN








- nullTue 14 Mar 2006 000925











Individual

Age

Sex

Race

Family Structure

Impulsivity

Family Income t-1

Risk Preference t-1

New Information




Updated Risk

Perceived Risk t

Baseline Risk

Perceived Risk t-1

Neighborhood


Mobility

Percent Black

Crime Rate



- nullTue 14 Mar 2006 000925

















- nullTue 14 Mar 2006 000925














- nullTue 14 Mar 2006 000925














- nullTue 14 Mar 2006 000925


















- nullTue 14 Mar 2006 000925





















- nullTue 14 Mar 2006 000925















not

not


- nullTue 14 Mar 2006 000925












u if yit ge u


it gt 1

1 if yit le 1









- nullTue 14 Mar 2006 000925















- nullTue 14 Mar 2006 000925



Tab

le 2

R

ando

m E

ffec

ts T

obit

Mod

els

of P

erce

ived

Cer

tain

ty o

f Arr

est (

Wav

es 3

ndash5)

Mod

el I

Mod

el I

IM

odel

III

The

fta

Vio

lenc

ebT

heft

aV

iole

nceb

The

fta

Vio

lenc

eb

Var

iabl

esmdash

Inte

rcep

t99

08

534

1

681

3

429

0

710

3

432

3

(82

1)(7

53)

(86

3)(7

73)

(86

0)(8

01)

mdashFe

mal

e11

18

6

72

9

00

5

25

586

3

36

(22

9)(2

11)

(20

0)(1

94)

(19

9)(1

96)

mdashA

ge

ndash38

9

ndash10

4ndash2

51

ndash0

45

ndash16

1

021

(0

44)

(04

0)(0

43)

(03

9)(0

43)

(04

1)mdash

No

Sib

ling

sndash3

25

ndash45

0ndash2

62

ndash45

8ndash2

92

ndash44

7(2

29)

(21

2)(2

03)

(19

6)(1

97)

(19

4)mdash

Res

iden

tial

Sta

bili

tyndash3

20

ndash11

3 ndash2

90

ndash11

1 ndash2

54

ndash09

6 (1

60)

(14

8)(1

39)

(13

4)(1

34)

(13

3)L

agge

d V

aria

bles

tndash1

mdashB

iolo

gica

l Par

ents

mdashmdash

229

2

54

022

1

24

(22

2)(2

11)

(21

7)(2

10)

mdashPa

rent

(s)

on W

elfa

remdash

mdash1

03

022

2

40

092

(2

95)

(27

2)(2

90)

(27

0)mdash

Fam

ily I

ncom

e ($

1000

s)mdash

mdashndash0

01

ndash00

4 0

00

ndash00

3 (0

06)

(00

6)(0

06)

(00

6)mdash

Ris

k P

refe

renc

emdash

mdashndash6

38

ndash6

40

ndash3

68

ndash44

1(2

04)

(18

9)(2

02)

(18

9)mdash

Pri

or P

erce

ived

Ris

k of

Arr

est

mdashmdash

029

017

027

016

(00

4)(0

04)

(00

4)(0

04)

Var

iabl

es a

t Tim

e t

mdashE

xper

ienc

ed C

erta

inty

mdashmdash

gt0

0ndash0

5mdash

mdashmdash

mdashndash1

573

ndash15

46

(3

46)

(32

7)mdash

mdash0

6ndash2

5mdash

mdashmdash

mdashndash8

42

ndash1

156

(33

1)(3

15)

mdashmdash

26ndash

10

mdashmdash

mdashmdash

ndash11

3 ndash4

32

(32

3)(3

02)

(con

tinu

ed o

n ne

xt p

age)


- nullTue 14 Mar 2006 000925



Tab

le 2

(C

onti

nued

)

Mod

el I

Mod

el I

IM

odel

III

The

fta

Vio

lenc

ebT

heft

aV

iole

nceb

The

fta

Vio

lenc

eb

Uns

anct

ione

d O

ffen

ses

mdashmdash

Low

Off

endi

ngmdash

mdashmdash

mdash1

10

ndash22

1 (3

13)

(28

9)mdash

mdashM

ediu

m O

ffen

ding

mdashmdash

mdashmdash

ndash37

6 ndash8

49

(3

21)

(30

0)mdash

mdashH

igh

Off

endi

ngmdash

mdashmdash

mdashndash1

002

ndash10

65

(3

01)

(28

6)mdash

Peer

Vio

lenc

eT

heft

mdashmdash

mdashmdash

ndash79

6

ndash3

00

(1

21)

(10

3)P

seud

o R

20

10

003

0

18

006

0

26

011

u i

SD

301

8

28

42

18

21

21

55

16

43

21

04

ε iS

D41

45

378

6 45

40

403

3 44

99

400

3

Wal

d

211

563

310

7

206

49

74

53

32

748

138

05

(d

f)(1

0)(1

0)(1

6)(1

6)(2

3)(2

3)lo

gndashli

keli

hood

ndash126

314

4 ndash1

3001

83

ndash125

957

8 ndash1

2982

38

ndash125

453

5 ndash1

2953

77

Not

eS

D =

sta

ndar

d de

viat

ion

df

= d

egre

es o

f fr

eedo

m S

tand

ard

erro

rs a

ppea

r in

par

enth

eses

Mod

els

incl

ude

(non

-sig

nifi

cant

) ef

fect

s of

bla

ck H

ispa

nic

con

cent

rate

d di

sadv

an-

tage

pro

port

ion

blac

k c

rim

e ra

te 1

984

and

hig

h im

puls

ivit

ya

N =

32

77

bN

= 3

281

plt

05

plt

01

plt

001

(tw

o-ta

iled

)


- nullTue 14 Mar 2006 000925




A

0

10

20

30

40

50

60

00 gt00-05 06-25 26-100


Per

ceiv

ed R

isk

of A

rres

t (T

heft

)


B

0

10

20

30

40

50

60

00 gt00-05 06-25 26-100


Per

ceiv

ed R

isk

of A

rres

t (V

iole

nce)



- nullTue 14 Mar 2006 000925










- nullTue 14 Mar 2006 000925











Individual

Age

Sex

Race

Family Structure

Impulsivity

Family Income t-1

Risk Preference t-1

Police Contact t-1

Rational Choice

Excitement t-1

Risk of Arrest t-1

Seen as Cool t-1

Employment t-1

Grades t-1

Opportunities t-1

Future Delinquency


Prior Delinquency


Neighborhood


Mobility

Percent Black

Crime Rate



- nullTue 14 Mar 2006 000925














- nullTue 14 Mar 2006 000925














(017) (017) (017) (017) (017) (017)mdashHispanic 022 021 021 024 022 023


(007) (007) (007) (007) (007) (007)mdashCrime Rate 1984 014 012 015 011 015 011










(010) (009) (010) (009)



- nullTue 14 Mar 2006 000925







Table 3 (Continued)








(003) (003)





- nullTue 14 Mar 2006 000925











- nullTue 14 Mar 2006 000925













- nullTue 14 Mar 2006 000925











- nullTue 14 Mar 2006 000925



AAPPPPEENNDDIIXX


Variables











mdashPeer Theftt

mdashPeer Violencet



mdashGradestndash1









mdashViolencet

mdashTheftt

Description





















- nullTue 14 Mar 2006 000925








































- nullTue 14 Mar 2006 000925









































- nullTue 14 Mar 2006 000925





































- nullTue 14 Mar 2006 000925













- nullTue 14 Mar 2006 000925







SSAANNCCTTIIOONN








- nullTue 14 Mar 2006 000925











Individual

Age

Sex

Race

Family Structure

Impulsivity

Family Income t-1

Risk Preference t-1

New Information




Updated Risk

Perceived Risk t

Baseline Risk

Perceived Risk t-1

Neighborhood


Mobility

Percent Black

Crime Rate



- nullTue 14 Mar 2006 000925

















- nullTue 14 Mar 2006 000925














- nullTue 14 Mar 2006 000925














- nullTue 14 Mar 2006 000925


















- nullTue 14 Mar 2006 000925





















- nullTue 14 Mar 2006 000925















not

not


- nullTue 14 Mar 2006 000925












u if yit ge u


it gt 1

1 if yit le 1









- nullTue 14 Mar 2006 000925















- nullTue 14 Mar 2006 000925



Tab

le 2

R

ando

m E

ffec

ts T

obit

Mod

els

of P

erce

ived

Cer

tain

ty o

f Arr

est (

Wav

es 3

ndash5)

Mod

el I

Mod

el I

IM

odel

III

The

fta

Vio

lenc

ebT

heft

aV

iole

nceb

The

fta

Vio

lenc

eb

Var

iabl

esmdash

Inte

rcep

t99

08

534

1

681

3

429

0

710

3

432

3

(82

1)(7

53)

(86

3)(7

73)

(86

0)(8

01)

mdashFe

mal

e11

18

6

72

9

00

5

25

586

3

36

(22

9)(2

11)

(20

0)(1

94)

(19

9)(1

96)

mdashA

ge

ndash38

9

ndash10

4ndash2

51

ndash0

45

ndash16

1

021

(0

44)

(04

0)(0

43)

(03

9)(0

43)

(04

1)mdash

No

Sib

ling

sndash3

25

ndash45

0ndash2

62

ndash45

8ndash2

92

ndash44

7(2

29)

(21

2)(2

03)

(19

6)(1

97)

(19

4)mdash

Res

iden

tial

Sta

bili

tyndash3

20

ndash11

3 ndash2

90

ndash11

1 ndash2

54

ndash09

6 (1

60)

(14

8)(1

39)

(13

4)(1

34)

(13

3)L

agge

d V

aria

bles

tndash1

mdashB

iolo

gica

l Par

ents

mdashmdash

229

2

54

022

1

24

(22

2)(2

11)

(21

7)(2

10)

mdashPa

rent

(s)

on W

elfa

remdash

mdash1

03

022

2

40

092

(2

95)

(27

2)(2

90)

(27

0)mdash

Fam

ily I

ncom

e ($

1000

s)mdash

mdashndash0

01

ndash00

4 0

00

ndash00

3 (0

06)

(00

6)(0

06)

(00

6)mdash

Ris

k P

refe

renc

emdash

mdashndash6

38

ndash6

40

ndash3

68

ndash44

1(2

04)

(18

9)(2

02)

(18

9)mdash

Pri

or P

erce

ived

Ris

k of

Arr

est

mdashmdash

029

017

027

016

(00

4)(0

04)

(00

4)(0

04)

Var

iabl

es a

t Tim

e t

mdashE

xper

ienc

ed C

erta

inty

mdashmdash

gt0

0ndash0

5mdash

mdashmdash

mdashndash1

573

ndash15

46

(3

46)

(32

7)mdash

mdash0

6ndash2

5mdash

mdashmdash

mdashndash8

42

ndash1

156

(33

1)(3

15)

mdashmdash

26ndash

10

mdashmdash

mdashmdash

ndash11

3 ndash4

32

(32

3)(3

02)

(con

tinu

ed o

n ne

xt p

age)


- nullTue 14 Mar 2006 000925



Tab

le 2

(C

onti

nued

)

Mod

el I

Mod

el I

IM

odel

III

The

fta

Vio

lenc

ebT

heft

aV

iole

nceb

The

fta

Vio

lenc

eb

Uns

anct

ione

d O

ffen

ses

mdashmdash

Low

Off

endi

ngmdash

mdashmdash

mdash1

10

ndash22

1 (3

13)

(28

9)mdash

mdashM

ediu

m O

ffen

ding

mdashmdash

mdashmdash

ndash37

6 ndash8

49

(3

21)

(30

0)mdash

mdashH

igh

Off

endi

ngmdash

mdashmdash

mdashndash1

002

ndash10

65

(3

01)

(28

6)mdash

Peer

Vio

lenc

eT

heft

mdashmdash

mdashmdash

ndash79

6

ndash3

00

(1

21)

(10

3)P

seud

o R

20

10

003

0

18

006

0

26

011

u i

SD

301

8

28

42

18

21

21

55

16

43

21

04

ε iS

D41

45

378

6 45

40

403

3 44

99

400

3

Wal

d

211

563

310

7

206

49

74

53

32

748

138

05

(d

f)(1

0)(1

0)(1

6)(1

6)(2

3)(2

3)lo

gndashli

keli

hood

ndash126

314

4 ndash1

3001

83

ndash125

957

8 ndash1

2982

38

ndash125

453

5 ndash1

2953

77

Not

eS

D =

sta

ndar

d de

viat

ion

df

= d

egre

es o

f fr

eedo

m S

tand

ard

erro

rs a

ppea

r in

par

enth

eses

Mod

els

incl

ude

(non

-sig

nifi

cant

) ef

fect

s of

bla

ck H

ispa

nic

con

cent

rate

d di

sadv

an-

tage

pro

port

ion

blac

k c

rim

e ra

te 1

984

and

hig

h im

puls

ivit

ya

N =

32

77

bN

= 3

281

plt

05

plt

01

plt

001

(tw

o-ta

iled

)


- nullTue 14 Mar 2006 000925




A

0

10

20

30

40

50

60

00 gt00-05 06-25 26-100


Per

ceiv

ed R

isk

of A

rres

t (T

heft

)


B

0

10

20

30

40

50

60

00 gt00-05 06-25 26-100


Per

ceiv

ed R

isk

of A

rres

t (V

iole

nce)



- nullTue 14 Mar 2006 000925










- nullTue 14 Mar 2006 000925











Individual

Age

Sex

Race

Family Structure

Impulsivity

Family Income t-1

Risk Preference t-1

Police Contact t-1

Rational Choice

Excitement t-1

Risk of Arrest t-1

Seen as Cool t-1

Employment t-1

Grades t-1

Opportunities t-1

Future Delinquency


Prior Delinquency


Neighborhood


Mobility

Percent Black

Crime Rate



- nullTue 14 Mar 2006 000925














- nullTue 14 Mar 2006 000925














(017) (017) (017) (017) (017) (017)mdashHispanic 022 021 021 024 022 023


(007) (007) (007) (007) (007) (007)mdashCrime Rate 1984 014 012 015 011 015 011










(010) (009) (010) (009)



- nullTue 14 Mar 2006 000925







Table 3 (Continued)








(003) (003)





- nullTue 14 Mar 2006 000925











- nullTue 14 Mar 2006 000925













- nullTue 14 Mar 2006 000925











- nullTue 14 Mar 2006 000925



AAPPPPEENNDDIIXX


Variables











mdashPeer Theftt

mdashPeer Violencet



mdashGradestndash1









mdashViolencet

mdashTheftt

Description





















- nullTue 14 Mar 2006 000925








































- nullTue 14 Mar 2006 000925









































- nullTue 14 Mar 2006 000925





































- nullTue 14 Mar 2006 000925







SSAANNCCTTIIOONN








- nullTue 14 Mar 2006 000925











Individual

Age

Sex

Race

Family Structure

Impulsivity

Family Income t-1

Risk Preference t-1

New Information




Updated Risk

Perceived Risk t

Baseline Risk

Perceived Risk t-1

Neighborhood


Mobility

Percent Black

Crime Rate



- nullTue 14 Mar 2006 000925

















- nullTue 14 Mar 2006 000925














- nullTue 14 Mar 2006 000925














- nullTue 14 Mar 2006 000925


















- nullTue 14 Mar 2006 000925





















- nullTue 14 Mar 2006 000925















not

not


- nullTue 14 Mar 2006 000925












u if yit ge u


it gt 1

1 if yit le 1









- nullTue 14 Mar 2006 000925















- nullTue 14 Mar 2006 000925



Tab

le 2

R

ando

m E

ffec

ts T

obit

Mod

els

of P

erce

ived

Cer

tain

ty o

f Arr

est (

Wav

es 3

ndash5)

Mod

el I

Mod

el I

IM

odel

III

The

fta

Vio

lenc

ebT

heft

aV

iole

nceb

The

fta

Vio

lenc

eb

Var

iabl

esmdash

Inte

rcep

t99

08

534

1

681

3

429

0

710

3

432

3

(82

1)(7

53)

(86

3)(7

73)

(86

0)(8

01)

mdashFe

mal

e11

18

6

72

9

00

5

25

586

3

36

(22

9)(2

11)

(20

0)(1

94)

(19

9)(1

96)

mdashA

ge

ndash38

9

ndash10

4ndash2

51

ndash0

45

ndash16

1

021

(0

44)

(04

0)(0

43)

(03

9)(0

43)

(04

1)mdash

No

Sib

ling

sndash3

25

ndash45

0ndash2

62

ndash45

8ndash2

92

ndash44

7(2

29)

(21

2)(2

03)

(19

6)(1

97)

(19

4)mdash

Res

iden

tial

Sta

bili

tyndash3

20

ndash11

3 ndash2

90

ndash11

1 ndash2

54

ndash09

6 (1

60)

(14

8)(1

39)

(13

4)(1

34)

(13

3)L

agge

d V

aria

bles

tndash1

mdashB

iolo

gica

l Par

ents

mdashmdash

229

2

54

022

1

24

(22

2)(2

11)

(21

7)(2

10)

mdashPa

rent

(s)

on W

elfa

remdash

mdash1

03

022

2

40

092

(2

95)

(27

2)(2

90)

(27

0)mdash

Fam

ily I

ncom

e ($

1000

s)mdash

mdashndash0

01

ndash00

4 0

00

ndash00

3 (0

06)

(00

6)(0

06)

(00

6)mdash

Ris

k P

refe

renc

emdash

mdashndash6

38

ndash6

40

ndash3

68

ndash44

1(2

04)

(18

9)(2

02)

(18

9)mdash

Pri

or P

erce

ived

Ris

k of

Arr

est

mdashmdash

029

017

027

016

(00

4)(0

04)

(00

4)(0

04)

Var

iabl

es a

t Tim

e t

mdashE

xper

ienc

ed C

erta

inty

mdashmdash

gt0

0ndash0

5mdash

mdashmdash

mdashndash1

573

ndash15

46

(3

46)

(32

7)mdash

mdash0

6ndash2

5mdash

mdashmdash

mdashndash8

42

ndash1

156

(33

1)(3

15)

mdashmdash

26ndash

10

mdashmdash

mdashmdash

ndash11

3 ndash4

32

(32

3)(3

02)

(con

tinu

ed o

n ne

xt p

age)


- nullTue 14 Mar 2006 000925



Tab

le 2

(C

onti

nued

)

Mod

el I

Mod

el I

IM

odel

III

The

fta

Vio

lenc

ebT

heft

aV

iole

nceb

The

fta

Vio

lenc

eb

Uns

anct

ione

d O

ffen

ses

mdashmdash

Low

Off

endi

ngmdash

mdashmdash

mdash1

10

ndash22

1 (3

13)

(28

9)mdash

mdashM

ediu

m O

ffen

ding

mdashmdash

mdashmdash

ndash37

6 ndash8

49

(3

21)

(30

0)mdash

mdashH

igh

Off

endi

ngmdash

mdashmdash

mdashndash1

002

ndash10

65

(3

01)

(28

6)mdash

Peer

Vio

lenc

eT

heft

mdashmdash

mdashmdash

ndash79

6

ndash3

00

(1

21)

(10

3)P

seud

o R

20

10

003

0

18

006

0

26

011

u i

SD

301

8

28

42

18

21

21

55

16

43

21

04

ε iS

D41

45

378

6 45

40

403

3 44

99

400

3

Wal

d

211

563

310

7

206

49

74

53

32

748

138

05

(d

f)(1

0)(1

0)(1

6)(1

6)(2

3)(2

3)lo

gndashli

keli

hood

ndash126

314

4 ndash1

3001

83

ndash125

957

8 ndash1

2982

38

ndash125

453

5 ndash1

2953

77

Not

eS

D =

sta

ndar

d de

viat

ion

df

= d

egre

es o

f fr

eedo

m S

tand

ard

erro

rs a

ppea

r in

par

enth

eses

Mod

els

incl

ude

(non

-sig

nifi

cant

) ef

fect

s of

bla

ck H

ispa

nic

con

cent

rate

d di

sadv

an-

tage

pro

port

ion

blac

k c

rim

e ra

te 1

984

and

hig

h im

puls

ivit

ya

N =

32

77

bN

= 3

281

plt

05

plt

01

plt

001

(tw

o-ta

iled

)


- nullTue 14 Mar 2006 000925




A

0

10

20

30

40

50

60

00 gt00-05 06-25 26-100


Per

ceiv

ed R

isk

of A

rres

t (T

heft

)


B

0

10

20

30

40

50

60

00 gt00-05 06-25 26-100


Per

ceiv

ed R

isk

of A

rres

t (V

iole

nce)



- nullTue 14 Mar 2006 000925










- nullTue 14 Mar 2006 000925











Individual

Age

Sex

Race

Family Structure

Impulsivity

Family Income t-1

Risk Preference t-1

Police Contact t-1

Rational Choice

Excitement t-1

Risk of Arrest t-1

Seen as Cool t-1

Employment t-1

Grades t-1

Opportunities t-1

Future Delinquency


Prior Delinquency


Neighborhood


Mobility

Percent Black

Crime Rate



- nullTue 14 Mar 2006 000925














- nullTue 14 Mar 2006 000925














(017) (017) (017) (017) (017) (017)mdashHispanic 022 021 021 024 022 023


(007) (007) (007) (007) (007) (007)mdashCrime Rate 1984 014 012 015 011 015 011










(010) (009) (010) (009)



- nullTue 14 Mar 2006 000925







Table 3 (Continued)








(003) (003)





- nullTue 14 Mar 2006 000925











- nullTue 14 Mar 2006 000925













- nullTue 14 Mar 2006 000925











- nullTue 14 Mar 2006 000925



AAPPPPEENNDDIIXX


Variables











mdashPeer Theftt

mdashPeer Violencet



mdashGradestndash1









mdashViolencet

mdashTheftt

Description





















- nullTue 14 Mar 2006 000925








































- nullTue 14 Mar 2006 000925









































- nullTue 14 Mar 2006 000925





































- nullTue 14 Mar 2006 000925











Individual

Age

Sex

Race

Family Structure

Impulsivity

Family Income t-1

Risk Preference t-1

New Information




Updated Risk

Perceived Risk t

Baseline Risk

Perceived Risk t-1

Neighborhood


Mobility

Percent Black

Crime Rate



- nullTue 14 Mar 2006 000925

















- nullTue 14 Mar 2006 000925














- nullTue 14 Mar 2006 000925














- nullTue 14 Mar 2006 000925


















- nullTue 14 Mar 2006 000925





















- nullTue 14 Mar 2006 000925















not

not


- nullTue 14 Mar 2006 000925












u if yit ge u


it gt 1

1 if yit le 1









- nullTue 14 Mar 2006 000925















- nullTue 14 Mar 2006 000925



Tab

le 2

R

ando

m E

ffec

ts T

obit

Mod

els

of P

erce

ived

Cer

tain

ty o

f Arr

est (

Wav

es 3

ndash5)

Mod

el I

Mod

el I

IM

odel

III

The

fta

Vio

lenc

ebT

heft

aV

iole

nceb

The

fta

Vio

lenc

eb

Var

iabl

esmdash

Inte

rcep

t99

08

534

1

681

3

429

0

710

3

432

3

(82

1)(7

53)

(86

3)(7

73)

(86

0)(8

01)

mdashFe

mal

e11

18

6

72

9

00

5

25

586

3

36

(22

9)(2

11)

(20

0)(1

94)

(19

9)(1

96)

mdashA

ge

ndash38

9

ndash10

4ndash2

51

ndash0

45

ndash16

1

021

(0

44)

(04

0)(0

43)

(03

9)(0

43)

(04

1)mdash

No

Sib

ling

sndash3

25

ndash45

0ndash2

62

ndash45

8ndash2

92

ndash44

7(2

29)

(21

2)(2

03)

(19

6)(1

97)

(19

4)mdash

Res

iden

tial

Sta

bili

tyndash3

20

ndash11

3 ndash2

90

ndash11

1 ndash2

54

ndash09

6 (1

60)

(14

8)(1

39)

(13

4)(1

34)

(13

3)L

agge

d V

aria

bles

tndash1

mdashB

iolo

gica

l Par

ents

mdashmdash

229

2

54

022

1

24

(22

2)(2

11)

(21

7)(2

10)

mdashPa

rent

(s)

on W

elfa

remdash

mdash1

03

022

2

40

092

(2

95)

(27

2)(2

90)

(27

0)mdash

Fam

ily I

ncom

e ($

1000

s)mdash

mdashndash0

01

ndash00

4 0

00

ndash00

3 (0

06)

(00

6)(0

06)

(00

6)mdash

Ris

k P

refe

renc

emdash

mdashndash6

38

ndash6

40

ndash3

68

ndash44

1(2

04)

(18

9)(2

02)

(18

9)mdash

Pri

or P

erce

ived

Ris

k of

Arr

est

mdashmdash

029

017

027

016

(00

4)(0

04)

(00

4)(0

04)

Var

iabl

es a

t Tim

e t

mdashE

xper

ienc

ed C

erta

inty

mdashmdash

gt0

0ndash0

5mdash

mdashmdash

mdashndash1

573

ndash15

46

(3

46)

(32

7)mdash

mdash0

6ndash2

5mdash

mdashmdash

mdashndash8

42

ndash1

156

(33

1)(3

15)

mdashmdash

26ndash

10

mdashmdash

mdashmdash

ndash11

3 ndash4

32

(32

3)(3

02)

(con

tinu

ed o

n ne

xt p

age)


- nullTue 14 Mar 2006 000925



Tab

le 2

(C

onti

nued

)

Mod

el I

Mod

el I

IM

odel

III

The

fta

Vio

lenc

ebT

heft

aV

iole

nceb

The

fta

Vio

lenc

eb

Uns

anct

ione

d O

ffen

ses

mdashmdash

Low

Off

endi

ngmdash

mdashmdash

mdash1

10

ndash22

1 (3

13)

(28

9)mdash

mdashM

ediu

m O

ffen

ding

mdashmdash

mdashmdash

ndash37

6 ndash8

49

(3

21)

(30

0)mdash

mdashH

igh

Off

endi

ngmdash

mdashmdash

mdashndash1

002

ndash10

65

(3

01)

(28

6)mdash

Peer

Vio

lenc

eT

heft

mdashmdash

mdashmdash

ndash79

6

ndash3

00

(1

21)

(10

3)P

seud

o R

20

10

003

0

18

006

0

26

011

u i

SD

301

8

28

42

18

21

21

55

16

43

21

04

ε iS

D41

45

378

6 45

40

403

3 44

99

400

3

Wal

d

211

563

310

7

206

49

74

53

32

748

138

05

(d

f)(1

0)(1

0)(1

6)(1

6)(2

3)(2

3)lo

gndashli

keli

hood

ndash126

314

4 ndash1

3001

83

ndash125

957

8 ndash1

2982

38

ndash125

453

5 ndash1

2953

77

Not

eS

D =

sta

ndar

d de

viat

ion

df

= d

egre

es o

f fr

eedo

m S

tand

ard

erro

rs a

ppea

r in

par

enth

eses

Mod

els

incl

ude

(non

-sig

nifi

cant

) ef

fect

s of

bla

ck H

ispa

nic

con

cent

rate

d di

sadv

an-

tage

pro

port

ion

blac

k c

rim

e ra

te 1

984

and

hig

h im

puls

ivit

ya

N =

32

77

bN

= 3

281

plt

05

plt

01

plt

001

(tw

o-ta

iled

)


- nullTue 14 Mar 2006 000925




A

0

10

20

30

40

50

60

00 gt00-05 06-25 26-100


Per

ceiv

ed R

isk

of A

rres

t (T

heft

)


B

0

10

20

30

40

50

60

00 gt00-05 06-25 26-100


Per

ceiv

ed R

isk

of A

rres

t (V

iole

nce)



- nullTue 14 Mar 2006 000925










- nullTue 14 Mar 2006 000925











Individual

Age

Sex

Race

Family Structure

Impulsivity

Family Income t-1

Risk Preference t-1

Police Contact t-1

Rational Choice

Excitement t-1

Risk of Arrest t-1

Seen as Cool t-1

Employment t-1

Grades t-1

Opportunities t-1

Future Delinquency


Prior Delinquency


Neighborhood


Mobility

Percent Black

Crime Rate



- nullTue 14 Mar 2006 000925














- nullTue 14 Mar 2006 000925














(017) (017) (017) (017) (017) (017)mdashHispanic 022 021 021 024 022 023


(007) (007) (007) (007) (007) (007)mdashCrime Rate 1984 014 012 015 011 015 011










(010) (009) (010) (009)



- nullTue 14 Mar 2006 000925







Table 3 (Continued)








(003) (003)





- nullTue 14 Mar 2006 000925











- nullTue 14 Mar 2006 000925













- nullTue 14 Mar 2006 000925











- nullTue 14 Mar 2006 000925



AAPPPPEENNDDIIXX


Variables











mdashPeer Theftt

mdashPeer Violencet



mdashGradestndash1









mdashViolencet

mdashTheftt

Description





















- nullTue 14 Mar 2006 000925








































- nullTue 14 Mar 2006 000925









































- nullTue 14 Mar 2006 000925





































- nullTue 14 Mar 2006 000925

















- nullTue 14 Mar 2006 000925














- nullTue 14 Mar 2006 000925














- nullTue 14 Mar 2006 000925


















- nullTue 14 Mar 2006 000925





















- nullTue 14 Mar 2006 000925















not

not


- nullTue 14 Mar 2006 000925












u if yit ge u


it gt 1

1 if yit le 1









- nullTue 14 Mar 2006 000925















- nullTue 14 Mar 2006 000925



Tab

le 2

R

ando

m E

ffec

ts T

obit

Mod

els

of P

erce

ived

Cer

tain

ty o

f Arr

est (

Wav

es 3

ndash5)

Mod

el I

Mod

el I

IM

odel

III

The

fta

Vio

lenc

ebT

heft

aV

iole

nceb

The

fta

Vio

lenc

eb

Var

iabl

esmdash

Inte

rcep

t99

08

534

1

681

3

429

0

710

3

432

3

(82

1)(7

53)

(86

3)(7

73)

(86

0)(8

01)

mdashFe

mal

e11

18

6

72

9

00

5

25

586

3

36

(22

9)(2

11)

(20

0)(1

94)

(19

9)(1

96)

mdashA

ge

ndash38

9

ndash10

4ndash2

51

ndash0

45

ndash16

1

021

(0

44)

(04

0)(0

43)

(03

9)(0

43)

(04

1)mdash

No

Sib

ling

sndash3

25

ndash45

0ndash2

62

ndash45

8ndash2

92

ndash44

7(2

29)

(21

2)(2

03)

(19

6)(1

97)

(19

4)mdash

Res

iden

tial

Sta

bili

tyndash3

20

ndash11

3 ndash2

90

ndash11

1 ndash2

54

ndash09

6 (1

60)

(14

8)(1

39)

(13

4)(1

34)

(13

3)L

agge

d V

aria

bles

tndash1

mdashB

iolo

gica

l Par

ents

mdashmdash

229

2

54

022

1

24

(22

2)(2

11)

(21

7)(2

10)

mdashPa

rent

(s)

on W

elfa

remdash

mdash1

03

022

2

40

092

(2

95)

(27

2)(2

90)

(27

0)mdash

Fam

ily I

ncom

e ($

1000

s)mdash

mdashndash0

01

ndash00

4 0

00

ndash00

3 (0

06)

(00

6)(0

06)

(00

6)mdash

Ris

k P

refe

renc

emdash

mdashndash6

38

ndash6

40

ndash3

68

ndash44

1(2

04)

(18

9)(2

02)

(18

9)mdash

Pri

or P

erce

ived

Ris

k of

Arr

est

mdashmdash

029

017

027

016

(00

4)(0

04)

(00

4)(0

04)

Var

iabl

es a

t Tim

e t

mdashE

xper

ienc

ed C

erta

inty

mdashmdash

gt0

0ndash0

5mdash

mdashmdash

mdashndash1

573

ndash15

46

(3

46)

(32

7)mdash

mdash0

6ndash2

5mdash

mdashmdash

mdashndash8

42

ndash1

156

(33

1)(3

15)

mdashmdash

26ndash

10

mdashmdash

mdashmdash

ndash11

3 ndash4

32

(32

3)(3

02)

(con

tinu

ed o

n ne

xt p

age)


- nullTue 14 Mar 2006 000925



Tab

le 2

(C

onti

nued

)

Mod

el I

Mod

el I

IM

odel

III

The

fta

Vio

lenc

ebT

heft

aV

iole

nceb

The

fta

Vio

lenc

eb

Uns

anct

ione

d O

ffen

ses

mdashmdash

Low

Off

endi

ngmdash

mdashmdash

mdash1

10

ndash22

1 (3

13)

(28

9)mdash

mdashM

ediu

m O

ffen

ding

mdashmdash

mdashmdash

ndash37

6 ndash8

49

(3

21)

(30

0)mdash

mdashH

igh

Off

endi

ngmdash

mdashmdash

mdashndash1

002

ndash10

65

(3

01)

(28

6)mdash

Peer

Vio

lenc

eT

heft

mdashmdash

mdashmdash

ndash79

6

ndash3

00

(1

21)

(10

3)P

seud

o R

20

10

003

0

18

006

0

26

011

u i

SD

301

8

28

42

18

21

21

55

16

43

21

04

ε iS

D41

45

378

6 45

40

403

3 44

99

400

3

Wal

d

211

563

310

7

206

49

74

53

32

748

138

05

(d

f)(1

0)(1

0)(1

6)(1

6)(2

3)(2

3)lo

gndashli

keli

hood

ndash126

314

4 ndash1

3001

83

ndash125

957

8 ndash1

2982

38

ndash125

453

5 ndash1

2953

77

Not

eS

D =

sta

ndar

d de

viat

ion

df

= d

egre

es o

f fr

eedo

m S

tand

ard

erro

rs a

ppea

r in

par

enth

eses

Mod

els

incl

ude

(non

-sig

nifi

cant

) ef

fect

s of

bla

ck H

ispa

nic

con

cent

rate

d di

sadv

an-

tage

pro

port

ion

blac

k c

rim

e ra

te 1

984

and

hig

h im

puls

ivit

ya

N =

32

77

bN

= 3

281

plt

05

plt

01

plt

001

(tw

o-ta

iled

)


- nullTue 14 Mar 2006 000925




A

0

10

20

30

40

50

60

00 gt00-05 06-25 26-100


Per

ceiv

ed R

isk

of A

rres

t (T

heft

)


B

0

10

20

30

40

50

60

00 gt00-05 06-25 26-100


Per

ceiv

ed R

isk

of A

rres

t (V

iole

nce)



- nullTue 14 Mar 2006 000925










- nullTue 14 Mar 2006 000925











Individual

Age

Sex

Race

Family Structure

Impulsivity

Family Income t-1

Risk Preference t-1

Police Contact t-1

Rational Choice

Excitement t-1

Risk of Arrest t-1

Seen as Cool t-1

Employment t-1

Grades t-1

Opportunities t-1

Future Delinquency


Prior Delinquency


Neighborhood


Mobility

Percent Black

Crime Rate



- nullTue 14 Mar 2006 000925














- nullTue 14 Mar 2006 000925














(017) (017) (017) (017) (017) (017)mdashHispanic 022 021 021 024 022 023


(007) (007) (007) (007) (007) (007)mdashCrime Rate 1984 014 012 015 011 015 011










(010) (009) (010) (009)



- nullTue 14 Mar 2006 000925







Table 3 (Continued)








(003) (003)





- nullTue 14 Mar 2006 000925











- nullTue 14 Mar 2006 000925













- nullTue 14 Mar 2006 000925











- nullTue 14 Mar 2006 000925



AAPPPPEENNDDIIXX


Variables











mdashPeer Theftt

mdashPeer Violencet



mdashGradestndash1









mdashViolencet

mdashTheftt

Description





















- nullTue 14 Mar 2006 000925








































- nullTue 14 Mar 2006 000925









































- nullTue 14 Mar 2006 000925





































- nullTue 14 Mar 2006 000925














- nullTue 14 Mar 2006 000925














- nullTue 14 Mar 2006 000925


















- nullTue 14 Mar 2006 000925





















- nullTue 14 Mar 2006 000925















not

not


- nullTue 14 Mar 2006 000925












u if yit ge u


it gt 1

1 if yit le 1









- nullTue 14 Mar 2006 000925















- nullTue 14 Mar 2006 000925



Tab

le 2

R

ando

m E

ffec

ts T

obit

Mod

els

of P

erce

ived

Cer

tain

ty o

f Arr

est (

Wav

es 3

ndash5)

Mod

el I

Mod

el I

IM

odel

III

The

fta

Vio

lenc

ebT

heft

aV

iole

nceb

The

fta

Vio

lenc

eb

Var

iabl

esmdash

Inte

rcep

t99

08

534

1

681

3

429

0

710

3

432

3

(82

1)(7

53)

(86

3)(7

73)

(86

0)(8

01)

mdashFe

mal

e11

18

6

72

9

00

5

25

586

3

36

(22

9)(2

11)

(20

0)(1

94)

(19

9)(1

96)

mdashA

ge

ndash38

9

ndash10

4ndash2

51

ndash0

45

ndash16

1

021

(0

44)

(04

0)(0

43)

(03

9)(0

43)

(04

1)mdash

No

Sib

ling

sndash3

25

ndash45

0ndash2

62

ndash45

8ndash2

92

ndash44

7(2

29)

(21

2)(2

03)

(19

6)(1

97)

(19

4)mdash

Res

iden

tial

Sta

bili

tyndash3

20

ndash11

3 ndash2

90

ndash11

1 ndash2

54

ndash09

6 (1

60)

(14

8)(1

39)

(13

4)(1

34)

(13

3)L

agge

d V

aria

bles

tndash1

mdashB

iolo

gica

l Par

ents

mdashmdash

229

2

54

022

1

24

(22

2)(2

11)

(21

7)(2

10)

mdashPa

rent

(s)

on W

elfa

remdash

mdash1

03

022

2

40

092

(2

95)

(27

2)(2

90)

(27

0)mdash

Fam

ily I

ncom

e ($

1000

s)mdash

mdashndash0

01

ndash00

4 0

00

ndash00

3 (0

06)

(00

6)(0

06)

(00

6)mdash

Ris

k P

refe

renc

emdash

mdashndash6

38

ndash6

40

ndash3

68

ndash44

1(2

04)

(18

9)(2

02)

(18

9)mdash

Pri

or P

erce

ived

Ris

k of

Arr

est

mdashmdash

029

017

027

016

(00

4)(0

04)

(00

4)(0

04)

Var

iabl

es a

t Tim

e t

mdashE

xper

ienc

ed C

erta

inty

mdashmdash

gt0

0ndash0

5mdash

mdashmdash

mdashndash1

573

ndash15

46

(3

46)

(32

7)mdash

mdash0

6ndash2

5mdash

mdashmdash

mdashndash8

42

ndash1

156

(33

1)(3

15)

mdashmdash

26ndash

10

mdashmdash

mdashmdash

ndash11

3 ndash4

32

(32

3)(3

02)

(con

tinu

ed o

n ne

xt p

age)


- nullTue 14 Mar 2006 000925



Tab

le 2

(C

onti

nued

)

Mod

el I

Mod

el I

IM

odel

III

The

fta

Vio

lenc

ebT

heft

aV

iole

nceb

The

fta

Vio

lenc

eb

Uns

anct

ione

d O

ffen

ses

mdashmdash

Low

Off

endi

ngmdash

mdashmdash

mdash1

10

ndash22

1 (3

13)

(28

9)mdash

mdashM

ediu

m O

ffen

ding

mdashmdash

mdashmdash

ndash37

6 ndash8

49

(3

21)

(30

0)mdash

mdashH

igh

Off

endi

ngmdash

mdashmdash

mdashndash1

002

ndash10

65

(3

01)

(28

6)mdash

Peer

Vio

lenc

eT

heft

mdashmdash

mdashmdash

ndash79

6

ndash3

00

(1

21)

(10

3)P

seud

o R

20

10

003

0

18

006

0

26

011

u i

SD

301

8

28

42

18

21

21

55

16

43

21

04

ε iS

D41

45

378

6 45

40

403

3 44

99

400

3

Wal

d

211

563

310

7

206

49

74

53

32

748

138

05

(d

f)(1

0)(1

0)(1

6)(1

6)(2

3)(2

3)lo

gndashli

keli

hood

ndash126

314

4 ndash1

3001

83

ndash125

957

8 ndash1

2982

38

ndash125

453

5 ndash1

2953

77

Not

eS

D =

sta

ndar

d de

viat

ion

df

= d

egre

es o

f fr

eedo

m S

tand

ard

erro

rs a

ppea

r in

par

enth

eses

Mod

els

incl

ude

(non

-sig

nifi

cant

) ef

fect

s of

bla

ck H

ispa

nic

con

cent

rate

d di

sadv

an-

tage

pro

port

ion

blac

k c

rim

e ra

te 1

984

and

hig

h im

puls

ivit

ya

N =

32

77

bN

= 3

281

plt

05

plt

01

plt

001

(tw

o-ta

iled

)


- nullTue 14 Mar 2006 000925




A

0

10

20

30

40

50

60

00 gt00-05 06-25 26-100


Per

ceiv

ed R

isk

of A

rres

t (T

heft

)


B

0

10

20

30

40

50

60

00 gt00-05 06-25 26-100


Per

ceiv

ed R

isk

of A

rres

t (V

iole

nce)



- nullTue 14 Mar 2006 000925










- nullTue 14 Mar 2006 000925











Individual

Age

Sex

Race

Family Structure

Impulsivity

Family Income t-1

Risk Preference t-1

Police Contact t-1

Rational Choice

Excitement t-1

Risk of Arrest t-1

Seen as Cool t-1

Employment t-1

Grades t-1

Opportunities t-1

Future Delinquency


Prior Delinquency


Neighborhood


Mobility

Percent Black

Crime Rate



- nullTue 14 Mar 2006 000925














- nullTue 14 Mar 2006 000925














(017) (017) (017) (017) (017) (017)mdashHispanic 022 021 021 024 022 023


(007) (007) (007) (007) (007) (007)mdashCrime Rate 1984 014 012 015 011 015 011










(010) (009) (010) (009)



- nullTue 14 Mar 2006 000925







Table 3 (Continued)








(003) (003)





- nullTue 14 Mar 2006 000925











- nullTue 14 Mar 2006 000925













- nullTue 14 Mar 2006 000925











- nullTue 14 Mar 2006 000925



AAPPPPEENNDDIIXX


Variables











mdashPeer Theftt

mdashPeer Violencet



mdashGradestndash1









mdashViolencet

mdashTheftt

Description





















- nullTue 14 Mar 2006 000925








































- nullTue 14 Mar 2006 000925









































- nullTue 14 Mar 2006 000925





































- nullTue 14 Mar 2006 000925














- nullTue 14 Mar 2006 000925


















- nullTue 14 Mar 2006 000925





















- nullTue 14 Mar 2006 000925















not

not


- nullTue 14 Mar 2006 000925












u if yit ge u


it gt 1

1 if yit le 1









- nullTue 14 Mar 2006 000925















- nullTue 14 Mar 2006 000925



Tab

le 2

R

ando

m E

ffec

ts T

obit

Mod

els

of P

erce

ived

Cer

tain

ty o

f Arr

est (

Wav

es 3

ndash5)

Mod

el I

Mod

el I

IM

odel

III

The

fta

Vio

lenc

ebT

heft

aV

iole

nceb

The

fta

Vio

lenc

eb

Var

iabl

esmdash

Inte

rcep

t99

08

534

1

681

3

429

0

710

3

432

3

(82

1)(7

53)

(86

3)(7

73)

(86

0)(8

01)

mdashFe

mal

e11

18

6

72

9

00

5

25

586

3

36

(22

9)(2

11)

(20

0)(1

94)

(19

9)(1

96)

mdashA

ge

ndash38

9

ndash10

4ndash2

51

ndash0

45

ndash16

1

021

(0

44)

(04

0)(0

43)

(03

9)(0

43)

(04

1)mdash

No

Sib

ling

sndash3

25

ndash45

0ndash2

62

ndash45

8ndash2

92

ndash44

7(2

29)

(21

2)(2

03)

(19

6)(1

97)

(19

4)mdash

Res

iden

tial

Sta

bili

tyndash3

20

ndash11

3 ndash2

90

ndash11

1 ndash2

54

ndash09

6 (1

60)

(14

8)(1

39)

(13

4)(1

34)

(13

3)L

agge

d V

aria

bles

tndash1

mdashB

iolo

gica

l Par

ents

mdashmdash

229

2

54

022

1

24

(22

2)(2

11)

(21

7)(2

10)

mdashPa

rent

(s)

on W

elfa

remdash

mdash1

03

022

2

40

092

(2

95)

(27

2)(2

90)

(27

0)mdash

Fam

ily I

ncom

e ($

1000

s)mdash

mdashndash0

01

ndash00

4 0

00

ndash00

3 (0

06)

(00

6)(0

06)

(00

6)mdash

Ris

k P

refe

renc

emdash

mdashndash6

38

ndash6

40

ndash3

68

ndash44

1(2

04)

(18

9)(2

02)

(18

9)mdash

Pri

or P

erce

ived

Ris

k of

Arr

est

mdashmdash

029

017

027

016

(00

4)(0

04)

(00

4)(0

04)

Var

iabl

es a

t Tim

e t

mdashE

xper

ienc

ed C

erta

inty

mdashmdash

gt0

0ndash0

5mdash

mdashmdash

mdashndash1

573

ndash15

46

(3

46)

(32

7)mdash

mdash0

6ndash2

5mdash

mdashmdash

mdashndash8

42

ndash1

156

(33

1)(3

15)

mdashmdash

26ndash

10

mdashmdash

mdashmdash

ndash11

3 ndash4

32

(32

3)(3

02)

(con

tinu

ed o

n ne

xt p

age)


- nullTue 14 Mar 2006 000925



Tab

le 2

(C

onti

nued

)

Mod

el I

Mod

el I

IM

odel

III

The

fta

Vio

lenc

ebT

heft

aV

iole

nceb

The

fta

Vio

lenc

eb

Uns

anct

ione

d O

ffen

ses

mdashmdash

Low

Off

endi

ngmdash

mdashmdash

mdash1

10

ndash22

1 (3

13)

(28

9)mdash

mdashM

ediu

m O

ffen

ding

mdashmdash

mdashmdash

ndash37

6 ndash8

49

(3

21)

(30

0)mdash

mdashH

igh

Off

endi

ngmdash

mdashmdash

mdashndash1

002

ndash10

65

(3

01)

(28

6)mdash

Peer

Vio

lenc

eT

heft

mdashmdash

mdashmdash

ndash79

6

ndash3

00

(1

21)

(10

3)P

seud

o R

20

10

003

0

18

006

0

26

011

u i

SD

301

8

28

42

18

21

21

55

16

43

21

04

ε iS

D41

45

378

6 45

40

403

3 44

99

400

3

Wal

d

211

563

310

7

206

49

74

53

32

748

138

05

(d

f)(1

0)(1

0)(1

6)(1

6)(2

3)(2

3)lo

gndashli

keli

hood

ndash126

314

4 ndash1

3001

83

ndash125

957

8 ndash1

2982

38

ndash125

453

5 ndash1

2953

77

Not

eS

D =

sta

ndar

d de

viat

ion

df

= d

egre

es o

f fr

eedo

m S

tand

ard

erro

rs a

ppea

r in

par

enth

eses

Mod

els

incl

ude

(non

-sig

nifi

cant

) ef

fect

s of

bla

ck H

ispa

nic

con

cent

rate

d di

sadv

an-

tage

pro

port

ion

blac

k c

rim

e ra

te 1

984

and

hig

h im

puls

ivit

ya

N =

32

77

bN

= 3

281

plt

05

plt

01

plt

001

(tw

o-ta

iled

)


- nullTue 14 Mar 2006 000925




A

0

10

20

30

40

50

60

00 gt00-05 06-25 26-100


Per

ceiv

ed R

isk

of A

rres

t (T

heft

)


B

0

10

20

30

40

50

60

00 gt00-05 06-25 26-100


Per

ceiv

ed R

isk

of A

rres

t (V

iole

nce)



- nullTue 14 Mar 2006 000925










- nullTue 14 Mar 2006 000925











Individual

Age

Sex

Race

Family Structure

Impulsivity

Family Income t-1

Risk Preference t-1

Police Contact t-1

Rational Choice

Excitement t-1

Risk of Arrest t-1

Seen as Cool t-1

Employment t-1

Grades t-1

Opportunities t-1

Future Delinquency


Prior Delinquency


Neighborhood


Mobility

Percent Black

Crime Rate



- nullTue 14 Mar 2006 000925














- nullTue 14 Mar 2006 000925














(017) (017) (017) (017) (017) (017)mdashHispanic 022 021 021 024 022 023


(007) (007) (007) (007) (007) (007)mdashCrime Rate 1984 014 012 015 011 015 011










(010) (009) (010) (009)



- nullTue 14 Mar 2006 000925







Table 3 (Continued)








(003) (003)





- nullTue 14 Mar 2006 000925











- nullTue 14 Mar 2006 000925













- nullTue 14 Mar 2006 000925











- nullTue 14 Mar 2006 000925



AAPPPPEENNDDIIXX


Variables











mdashPeer Theftt

mdashPeer Violencet



mdashGradestndash1









mdashViolencet

mdashTheftt

Description





















- nullTue 14 Mar 2006 000925








































- nullTue 14 Mar 2006 000925









































- nullTue 14 Mar 2006 000925





































- nullTue 14 Mar 2006 000925


















- nullTue 14 Mar 2006 000925





















- nullTue 14 Mar 2006 000925















not

not


- nullTue 14 Mar 2006 000925












u if yit ge u


it gt 1

1 if yit le 1









- nullTue 14 Mar 2006 000925















- nullTue 14 Mar 2006 000925



Tab

le 2

R

ando

m E

ffec

ts T

obit

Mod

els

of P

erce

ived

Cer

tain

ty o

f Arr

est (

Wav

es 3

ndash5)

Mod

el I

Mod

el I

IM

odel

III

The

fta

Vio

lenc

ebT

heft

aV

iole

nceb

The

fta

Vio

lenc

eb

Var

iabl

esmdash

Inte

rcep

t99

08

534

1

681

3

429

0

710

3

432

3

(82

1)(7

53)

(86

3)(7

73)

(86

0)(8

01)

mdashFe

mal

e11

18

6

72

9

00

5

25

586

3

36

(22

9)(2

11)

(20

0)(1

94)

(19

9)(1

96)

mdashA

ge

ndash38

9

ndash10

4ndash2

51

ndash0

45

ndash16

1

021

(0

44)

(04

0)(0

43)

(03

9)(0

43)

(04

1)mdash

No

Sib

ling

sndash3

25

ndash45

0ndash2

62

ndash45

8ndash2

92

ndash44

7(2

29)

(21

2)(2

03)

(19

6)(1

97)

(19

4)mdash

Res

iden

tial

Sta

bili

tyndash3

20

ndash11

3 ndash2

90

ndash11

1 ndash2

54

ndash09

6 (1

60)

(14

8)(1

39)

(13

4)(1

34)

(13

3)L

agge

d V

aria

bles

tndash1

mdashB

iolo

gica

l Par

ents

mdashmdash

229

2

54

022

1

24

(22

2)(2

11)

(21

7)(2

10)

mdashPa

rent

(s)

on W

elfa

remdash

mdash1

03

022

2

40

092

(2

95)

(27

2)(2

90)

(27

0)mdash

Fam

ily I

ncom

e ($

1000

s)mdash

mdashndash0

01

ndash00

4 0

00

ndash00

3 (0

06)

(00

6)(0

06)

(00

6)mdash

Ris

k P

refe

renc

emdash

mdashndash6

38

ndash6

40

ndash3

68

ndash44

1(2

04)

(18

9)(2

02)

(18

9)mdash

Pri

or P

erce

ived

Ris

k of

Arr

est

mdashmdash

029

017

027

016

(00

4)(0

04)

(00

4)(0

04)

Var

iabl

es a

t Tim

e t

mdashE

xper

ienc

ed C

erta

inty

mdashmdash

gt0

0ndash0

5mdash

mdashmdash

mdashndash1

573

ndash15

46

(3

46)

(32

7)mdash

mdash0

6ndash2

5mdash

mdashmdash

mdashndash8

42

ndash1

156

(33

1)(3

15)

mdashmdash

26ndash

10

mdashmdash

mdashmdash

ndash11

3 ndash4

32

(32

3)(3

02)

(con

tinu

ed o

n ne

xt p

age)


- nullTue 14 Mar 2006 000925



Tab

le 2

(C

onti

nued

)

Mod

el I

Mod

el I

IM

odel

III

The

fta

Vio

lenc

ebT

heft

aV

iole

nceb

The

fta

Vio

lenc

eb

Uns

anct

ione

d O

ffen

ses

mdashmdash

Low

Off

endi

ngmdash

mdashmdash

mdash1

10

ndash22

1 (3

13)

(28

9)mdash

mdashM

ediu

m O

ffen

ding

mdashmdash

mdashmdash

ndash37

6 ndash8

49

(3

21)

(30

0)mdash

mdashH

igh

Off

endi

ngmdash

mdashmdash

mdashndash1

002

ndash10

65

(3

01)

(28

6)mdash

Peer

Vio

lenc

eT

heft

mdashmdash

mdashmdash

ndash79

6

ndash3

00

(1

21)

(10

3)P

seud

o R

20

10

003

0

18

006

0

26

011

u i

SD

301

8

28

42

18

21

21

55

16

43

21

04

ε iS

D41

45

378

6 45

40

403

3 44

99

400

3

Wal

d

211

563

310

7

206

49

74

53

32

748

138

05

(d

f)(1

0)(1

0)(1

6)(1

6)(2

3)(2

3)lo

gndashli

keli

hood

ndash126

314

4 ndash1

3001

83

ndash125

957

8 ndash1

2982

38

ndash125

453

5 ndash1

2953

77

Not

eS

D =

sta

ndar

d de

viat

ion

df

= d

egre

es o

f fr

eedo

m S

tand

ard

erro

rs a

ppea

r in

par

enth

eses

Mod

els

incl

ude

(non

-sig

nifi

cant

) ef

fect

s of

bla

ck H

ispa

nic

con

cent

rate

d di

sadv

an-

tage

pro

port

ion

blac

k c

rim

e ra

te 1

984

and

hig

h im

puls

ivit

ya

N =

32

77

bN

= 3

281

plt

05

plt

01

plt

001

(tw

o-ta

iled

)


- nullTue 14 Mar 2006 000925




A

0

10

20

30

40

50

60

00 gt00-05 06-25 26-100


Per

ceiv

ed R

isk

of A

rres

t (T

heft

)


B

0

10

20

30

40

50

60

00 gt00-05 06-25 26-100


Per

ceiv

ed R

isk

of A

rres

t (V

iole

nce)



- nullTue 14 Mar 2006 000925










- nullTue 14 Mar 2006 000925











Individual

Age

Sex

Race

Family Structure

Impulsivity

Family Income t-1

Risk Preference t-1

Police Contact t-1

Rational Choice

Excitement t-1

Risk of Arrest t-1

Seen as Cool t-1

Employment t-1

Grades t-1

Opportunities t-1

Future Delinquency


Prior Delinquency


Neighborhood


Mobility

Percent Black

Crime Rate



- nullTue 14 Mar 2006 000925














- nullTue 14 Mar 2006 000925














(017) (017) (017) (017) (017) (017)mdashHispanic 022 021 021 024 022 023


(007) (007) (007) (007) (007) (007)mdashCrime Rate 1984 014 012 015 011 015 011










(010) (009) (010) (009)



- nullTue 14 Mar 2006 000925







Table 3 (Continued)








(003) (003)





- nullTue 14 Mar 2006 000925











- nullTue 14 Mar 2006 000925













- nullTue 14 Mar 2006 000925











- nullTue 14 Mar 2006 000925



AAPPPPEENNDDIIXX


Variables











mdashPeer Theftt

mdashPeer Violencet



mdashGradestndash1









mdashViolencet

mdashTheftt

Description





















- nullTue 14 Mar 2006 000925








































- nullTue 14 Mar 2006 000925









































- nullTue 14 Mar 2006 000925





































- nullTue 14 Mar 2006 000925





















- nullTue 14 Mar 2006 000925















not

not


- nullTue 14 Mar 2006 000925












u if yit ge u


it gt 1

1 if yit le 1









- nullTue 14 Mar 2006 000925















- nullTue 14 Mar 2006 000925



Tab

le 2

R

ando

m E

ffec

ts T

obit

Mod

els

of P

erce

ived

Cer

tain

ty o

f Arr

est (

Wav

es 3

ndash5)

Mod

el I

Mod

el I

IM

odel

III

The

fta

Vio

lenc

ebT

heft

aV

iole

nceb

The

fta

Vio

lenc

eb

Var

iabl

esmdash

Inte

rcep

t99

08

534

1

681

3

429

0

710

3

432

3

(82

1)(7

53)

(86

3)(7

73)

(86

0)(8

01)

mdashFe

mal

e11

18

6

72

9

00

5

25

586

3

36

(22

9)(2

11)

(20

0)(1

94)

(19

9)(1

96)

mdashA

ge

ndash38

9

ndash10

4ndash2

51

ndash0

45

ndash16

1

021

(0

44)

(04

0)(0

43)

(03

9)(0

43)

(04

1)mdash

No

Sib

ling

sndash3

25

ndash45

0ndash2

62

ndash45

8ndash2

92

ndash44

7(2

29)

(21

2)(2

03)

(19

6)(1

97)

(19

4)mdash

Res

iden

tial

Sta

bili

tyndash3

20

ndash11

3 ndash2

90

ndash11

1 ndash2

54

ndash09

6 (1

60)

(14

8)(1

39)

(13

4)(1

34)

(13

3)L

agge

d V

aria

bles

tndash1

mdashB

iolo

gica

l Par

ents

mdashmdash

229

2

54

022

1

24

(22

2)(2

11)

(21

7)(2

10)

mdashPa

rent

(s)

on W

elfa

remdash

mdash1

03

022

2

40

092

(2

95)

(27

2)(2

90)

(27

0)mdash

Fam

ily I

ncom

e ($

1000

s)mdash

mdashndash0

01

ndash00

4 0

00

ndash00

3 (0

06)

(00

6)(0

06)

(00

6)mdash

Ris

k P

refe

renc

emdash

mdashndash6

38

ndash6

40

ndash3

68

ndash44

1(2

04)

(18

9)(2

02)

(18

9)mdash

Pri

or P

erce

ived

Ris

k of

Arr

est

mdashmdash

029

017

027

016

(00

4)(0

04)

(00

4)(0

04)

Var

iabl

es a

t Tim

e t

mdashE

xper

ienc

ed C

erta

inty

mdashmdash

gt0

0ndash0

5mdash

mdashmdash

mdashndash1

573

ndash15

46

(3

46)

(32

7)mdash

mdash0

6ndash2

5mdash

mdashmdash

mdashndash8

42

ndash1

156

(33

1)(3

15)

mdashmdash

26ndash

10

mdashmdash

mdashmdash

ndash11

3 ndash4

32

(32

3)(3

02)

(con

tinu

ed o

n ne

xt p

age)


- nullTue 14 Mar 2006 000925



Tab

le 2

(C

onti

nued

)

Mod

el I

Mod

el I

IM

odel

III

The

fta

Vio

lenc

ebT

heft

aV

iole

nceb

The

fta

Vio

lenc

eb

Uns

anct

ione

d O

ffen

ses

mdashmdash

Low

Off

endi

ngmdash

mdashmdash

mdash1

10

ndash22

1 (3

13)

(28

9)mdash

mdashM

ediu

m O

ffen

ding

mdashmdash

mdashmdash

ndash37

6 ndash8

49

(3

21)

(30

0)mdash

mdashH

igh

Off

endi

ngmdash

mdashmdash

mdashndash1

002

ndash10

65

(3

01)

(28

6)mdash

Peer

Vio

lenc

eT

heft

mdashmdash

mdashmdash

ndash79

6

ndash3

00

(1

21)

(10

3)P

seud

o R

20

10

003

0

18

006

0

26

011

u i

SD

301

8

28

42

18

21

21

55

16

43

21

04

ε iS

D41

45

378

6 45

40

403

3 44

99

400

3

Wal

d

211

563

310

7

206

49

74

53

32

748

138

05

(d

f)(1

0)(1

0)(1

6)(1

6)(2

3)(2

3)lo

gndashli

keli

hood

ndash126

314

4 ndash1

3001

83

ndash125

957

8 ndash1

2982

38

ndash125

453

5 ndash1

2953

77

Not

eS

D =

sta

ndar

d de

viat

ion

df

= d

egre

es o

f fr

eedo

m S

tand

ard

erro

rs a

ppea

r in

par

enth

eses

Mod

els

incl

ude

(non

-sig

nifi

cant

) ef

fect

s of

bla

ck H

ispa

nic

con

cent

rate

d di

sadv

an-

tage

pro

port

ion

blac

k c

rim

e ra

te 1

984

and

hig

h im

puls

ivit

ya

N =

32

77

bN

= 3

281

plt

05

plt

01

plt

001

(tw

o-ta

iled

)


- nullTue 14 Mar 2006 000925




A

0

10

20

30

40

50

60

00 gt00-05 06-25 26-100


Per

ceiv

ed R

isk

of A

rres

t (T

heft

)


B

0

10

20

30

40

50

60

00 gt00-05 06-25 26-100


Per

ceiv

ed R

isk

of A

rres

t (V

iole

nce)



- nullTue 14 Mar 2006 000925










- nullTue 14 Mar 2006 000925











Individual

Age

Sex

Race

Family Structure

Impulsivity

Family Income t-1

Risk Preference t-1

Police Contact t-1

Rational Choice

Excitement t-1

Risk of Arrest t-1

Seen as Cool t-1

Employment t-1

Grades t-1

Opportunities t-1

Future Delinquency


Prior Delinquency


Neighborhood


Mobility

Percent Black

Crime Rate



- nullTue 14 Mar 2006 000925














- nullTue 14 Mar 2006 000925














(017) (017) (017) (017) (017) (017)mdashHispanic 022 021 021 024 022 023


(007) (007) (007) (007) (007) (007)mdashCrime Rate 1984 014 012 015 011 015 011










(010) (009) (010) (009)



- nullTue 14 Mar 2006 000925







Table 3 (Continued)








(003) (003)





- nullTue 14 Mar 2006 000925











- nullTue 14 Mar 2006 000925













- nullTue 14 Mar 2006 000925











- nullTue 14 Mar 2006 000925



AAPPPPEENNDDIIXX


Variables











mdashPeer Theftt

mdashPeer Violencet



mdashGradestndash1









mdashViolencet

mdashTheftt

Description





















- nullTue 14 Mar 2006 000925








































- nullTue 14 Mar 2006 000925









































- nullTue 14 Mar 2006 000925





































- nullTue 14 Mar 2006 000925















not

not


- nullTue 14 Mar 2006 000925












u if yit ge u


it gt 1

1 if yit le 1









- nullTue 14 Mar 2006 000925















- nullTue 14 Mar 2006 000925



Tab

le 2

R

ando

m E

ffec

ts T

obit

Mod

els

of P

erce

ived

Cer

tain

ty o

f Arr

est (

Wav

es 3

ndash5)

Mod

el I

Mod

el I

IM

odel

III

The

fta

Vio

lenc

ebT

heft

aV

iole

nceb

The

fta

Vio

lenc

eb

Var

iabl

esmdash

Inte

rcep

t99

08

534

1

681

3

429

0

710

3

432

3

(82

1)(7

53)

(86

3)(7

73)

(86

0)(8

01)

mdashFe

mal

e11

18

6

72

9

00

5

25

586

3

36

(22

9)(2

11)

(20

0)(1

94)

(19

9)(1

96)

mdashA

ge

ndash38

9

ndash10

4ndash2

51

ndash0

45

ndash16

1

021

(0

44)

(04

0)(0

43)

(03

9)(0

43)

(04

1)mdash

No

Sib

ling

sndash3

25

ndash45

0ndash2

62

ndash45

8ndash2

92

ndash44

7(2

29)

(21

2)(2

03)

(19

6)(1

97)

(19

4)mdash

Res

iden

tial

Sta

bili

tyndash3

20

ndash11

3 ndash2

90

ndash11

1 ndash2

54

ndash09

6 (1

60)

(14

8)(1

39)

(13

4)(1

34)

(13

3)L

agge

d V

aria

bles

tndash1

mdashB

iolo

gica

l Par

ents

mdashmdash

229

2

54

022

1

24

(22

2)(2

11)

(21

7)(2

10)

mdashPa

rent

(s)

on W

elfa

remdash

mdash1

03

022

2

40

092

(2

95)

(27

2)(2

90)

(27

0)mdash

Fam

ily I

ncom

e ($

1000

s)mdash

mdashndash0

01

ndash00

4 0

00

ndash00

3 (0

06)

(00

6)(0

06)

(00

6)mdash

Ris

k P

refe

renc

emdash

mdashndash6

38

ndash6

40

ndash3

68

ndash44

1(2

04)

(18

9)(2

02)

(18

9)mdash

Pri

or P

erce

ived

Ris

k of

Arr

est

mdashmdash

029

017

027

016

(00

4)(0

04)

(00

4)(0

04)

Var

iabl

es a

t Tim

e t

mdashE

xper

ienc

ed C

erta

inty

mdashmdash

gt0

0ndash0

5mdash

mdashmdash

mdashndash1

573

ndash15

46

(3

46)

(32

7)mdash

mdash0

6ndash2

5mdash

mdashmdash

mdashndash8

42

ndash1

156

(33

1)(3

15)

mdashmdash

26ndash

10

mdashmdash

mdashmdash

ndash11

3 ndash4

32

(32

3)(3

02)

(con

tinu

ed o

n ne

xt p

age)


- nullTue 14 Mar 2006 000925



Tab

le 2

(C

onti

nued

)

Mod

el I

Mod

el I

IM

odel

III

The

fta

Vio

lenc

ebT

heft

aV

iole

nceb

The

fta

Vio

lenc

eb

Uns

anct

ione

d O

ffen

ses

mdashmdash

Low

Off

endi

ngmdash

mdashmdash

mdash1

10

ndash22

1 (3

13)

(28

9)mdash

mdashM

ediu

m O

ffen

ding

mdashmdash

mdashmdash

ndash37

6 ndash8

49

(3

21)

(30

0)mdash

mdashH

igh

Off

endi

ngmdash

mdashmdash

mdashndash1

002

ndash10

65

(3

01)

(28

6)mdash

Peer

Vio

lenc

eT

heft

mdashmdash

mdashmdash

ndash79

6

ndash3

00

(1

21)

(10

3)P

seud

o R

20

10

003

0

18

006

0

26

011

u i

SD

301

8

28

42

18

21

21

55

16

43

21

04

ε iS

D41

45

378

6 45

40

403

3 44

99

400

3

Wal

d

211

563

310

7

206

49

74

53

32

748

138

05

(d

f)(1

0)(1

0)(1

6)(1

6)(2

3)(2

3)lo

gndashli

keli

hood

ndash126

314

4 ndash1

3001

83

ndash125

957

8 ndash1

2982

38

ndash125

453

5 ndash1

2953

77

Not

eS

D =

sta

ndar

d de

viat

ion

df

= d

egre

es o

f fr

eedo

m S

tand

ard

erro

rs a

ppea

r in

par

enth

eses

Mod

els

incl

ude

(non

-sig

nifi

cant

) ef

fect

s of

bla

ck H

ispa

nic

con

cent

rate

d di

sadv

an-

tage

pro

port

ion

blac

k c

rim

e ra

te 1

984

and

hig

h im

puls

ivit

ya

N =

32

77

bN

= 3

281

plt

05

plt

01

plt

001

(tw

o-ta

iled

)


- nullTue 14 Mar 2006 000925




A

0

10

20

30

40

50

60

00 gt00-05 06-25 26-100


Per

ceiv

ed R

isk

of A

rres

t (T

heft

)


B

0

10

20

30

40

50

60

00 gt00-05 06-25 26-100


Per

ceiv

ed R

isk

of A

rres

t (V

iole

nce)



- nullTue 14 Mar 2006 000925










- nullTue 14 Mar 2006 000925











Individual

Age

Sex

Race

Family Structure

Impulsivity

Family Income t-1

Risk Preference t-1

Police Contact t-1

Rational Choice

Excitement t-1

Risk of Arrest t-1

Seen as Cool t-1

Employment t-1

Grades t-1

Opportunities t-1

Future Delinquency


Prior Delinquency


Neighborhood


Mobility

Percent Black

Crime Rate



- nullTue 14 Mar 2006 000925














- nullTue 14 Mar 2006 000925














(017) (017) (017) (017) (017) (017)mdashHispanic 022 021 021 024 022 023


(007) (007) (007) (007) (007) (007)mdashCrime Rate 1984 014 012 015 011 015 011










(010) (009) (010) (009)



- nullTue 14 Mar 2006 000925







Table 3 (Continued)








(003) (003)





- nullTue 14 Mar 2006 000925











- nullTue 14 Mar 2006 000925













- nullTue 14 Mar 2006 000925











- nullTue 14 Mar 2006 000925



AAPPPPEENNDDIIXX


Variables











mdashPeer Theftt

mdashPeer Violencet



mdashGradestndash1









mdashViolencet

mdashTheftt

Description





















- nullTue 14 Mar 2006 000925








































- nullTue 14 Mar 2006 000925









































- nullTue 14 Mar 2006 000925





































- nullTue 14 Mar 2006 000925












u if yit ge u


it gt 1

1 if yit le 1









- nullTue 14 Mar 2006 000925















- nullTue 14 Mar 2006 000925



Tab

le 2

R

ando

m E

ffec

ts T

obit

Mod

els

of P

erce

ived

Cer

tain

ty o

f Arr

est (

Wav

es 3

ndash5)

Mod

el I

Mod

el I

IM

odel

III

The

fta

Vio

lenc

ebT

heft

aV

iole

nceb

The

fta

Vio

lenc

eb

Var

iabl

esmdash

Inte

rcep

t99

08

534

1

681

3

429

0

710

3

432

3

(82

1)(7

53)

(86

3)(7

73)

(86

0)(8

01)

mdashFe

mal

e11

18

6

72

9

00

5

25

586

3

36

(22

9)(2

11)

(20

0)(1

94)

(19

9)(1

96)

mdashA

ge

ndash38

9

ndash10

4ndash2

51

ndash0

45

ndash16

1

021

(0

44)

(04

0)(0

43)

(03

9)(0

43)

(04

1)mdash

No

Sib

ling

sndash3

25

ndash45

0ndash2

62

ndash45

8ndash2

92

ndash44

7(2

29)

(21

2)(2

03)

(19

6)(1

97)

(19

4)mdash

Res

iden

tial

Sta

bili

tyndash3

20

ndash11

3 ndash2

90

ndash11

1 ndash2

54

ndash09

6 (1

60)

(14

8)(1

39)

(13

4)(1

34)

(13

3)L

agge

d V

aria

bles

tndash1

mdashB

iolo

gica

l Par

ents

mdashmdash

229

2

54

022

1

24

(22

2)(2

11)

(21

7)(2

10)

mdashPa

rent

(s)

on W

elfa

remdash

mdash1

03

022

2

40

092

(2

95)

(27

2)(2

90)

(27

0)mdash

Fam

ily I

ncom

e ($

1000

s)mdash

mdashndash0

01

ndash00

4 0

00

ndash00

3 (0

06)

(00

6)(0

06)

(00

6)mdash

Ris

k P

refe

renc

emdash

mdashndash6

38

ndash6

40

ndash3

68

ndash44

1(2

04)

(18

9)(2

02)

(18

9)mdash

Pri

or P

erce

ived

Ris

k of

Arr

est

mdashmdash

029

017

027

016

(00

4)(0

04)

(00

4)(0

04)

Var

iabl

es a

t Tim

e t

mdashE

xper

ienc

ed C

erta

inty

mdashmdash

gt0

0ndash0

5mdash

mdashmdash

mdashndash1

573

ndash15

46

(3

46)

(32

7)mdash

mdash0

6ndash2

5mdash

mdashmdash

mdashndash8

42

ndash1

156

(33

1)(3

15)

mdashmdash

26ndash

10

mdashmdash

mdashmdash

ndash11

3 ndash4

32

(32

3)(3

02)

(con

tinu

ed o

n ne

xt p

age)


- nullTue 14 Mar 2006 000925



Tab

le 2

(C

onti

nued

)

Mod

el I

Mod

el I

IM

odel

III

The

fta

Vio

lenc

ebT

heft

aV

iole

nceb

The

fta

Vio

lenc

eb

Uns

anct

ione

d O

ffen

ses

mdashmdash

Low

Off

endi

ngmdash

mdashmdash

mdash1

10

ndash22

1 (3

13)

(28

9)mdash

mdashM

ediu

m O

ffen

ding

mdashmdash

mdashmdash

ndash37

6 ndash8

49

(3

21)

(30

0)mdash

mdashH

igh

Off

endi

ngmdash

mdashmdash

mdashndash1

002

ndash10

65

(3

01)

(28

6)mdash

Peer

Vio

lenc

eT

heft

mdashmdash

mdashmdash

ndash79

6

ndash3

00

(1

21)

(10

3)P

seud

o R

20

10

003

0

18

006

0

26

011

u i

SD

301

8

28

42

18

21

21

55

16

43

21

04

ε iS

D41

45

378

6 45

40

403

3 44

99

400

3

Wal

d

211

563

310

7

206

49

74

53

32

748

138

05

(d

f)(1

0)(1

0)(1

6)(1

6)(2

3)(2

3)lo

gndashli

keli

hood

ndash126

314

4 ndash1

3001

83

ndash125

957

8 ndash1

2982

38

ndash125

453

5 ndash1

2953

77

Not

eS

D =

sta

ndar

d de

viat

ion

df

= d

egre

es o

f fr

eedo

m S

tand

ard

erro

rs a

ppea

r in

par

enth

eses

Mod

els

incl

ude

(non

-sig

nifi

cant

) ef

fect

s of

bla

ck H

ispa

nic

con

cent

rate

d di

sadv

an-

tage

pro

port

ion

blac

k c

rim

e ra

te 1

984

and

hig

h im

puls

ivit

ya

N =

32

77

bN

= 3

281

plt

05

plt

01

plt

001

(tw

o-ta

iled

)


- nullTue 14 Mar 2006 000925




A

0

10

20

30

40

50

60

00 gt00-05 06-25 26-100


Per

ceiv

ed R

isk

of A

rres

t (T

heft

)


B

0

10

20

30

40

50

60

00 gt00-05 06-25 26-100


Per

ceiv

ed R

isk

of A

rres

t (V

iole

nce)



- nullTue 14 Mar 2006 000925










- nullTue 14 Mar 2006 000925











Individual

Age

Sex

Race

Family Structure

Impulsivity

Family Income t-1

Risk Preference t-1

Police Contact t-1

Rational Choice

Excitement t-1

Risk of Arrest t-1

Seen as Cool t-1

Employment t-1

Grades t-1

Opportunities t-1

Future Delinquency


Prior Delinquency


Neighborhood


Mobility

Percent Black

Crime Rate



- nullTue 14 Mar 2006 000925














- nullTue 14 Mar 2006 000925














(017) (017) (017) (017) (017) (017)mdashHispanic 022 021 021 024 022 023


(007) (007) (007) (007) (007) (007)mdashCrime Rate 1984 014 012 015 011 015 011










(010) (009) (010) (009)



- nullTue 14 Mar 2006 000925







Table 3 (Continued)








(003) (003)





- nullTue 14 Mar 2006 000925











- nullTue 14 Mar 2006 000925













- nullTue 14 Mar 2006 000925











- nullTue 14 Mar 2006 000925



AAPPPPEENNDDIIXX


Variables











mdashPeer Theftt

mdashPeer Violencet



mdashGradestndash1









mdashViolencet

mdashTheftt

Description





















- nullTue 14 Mar 2006 000925








































- nullTue 14 Mar 2006 000925









































- nullTue 14 Mar 2006 000925





































- nullTue 14 Mar 2006 000925















- nullTue 14 Mar 2006 000925



Tab

le 2

R

ando

m E

ffec

ts T

obit

Mod

els

of P

erce

ived

Cer

tain

ty o

f Arr

est (

Wav

es 3

ndash5)

Mod

el I

Mod

el I

IM

odel

III

The

fta

Vio

lenc

ebT

heft

aV

iole

nceb

The

fta

Vio

lenc

eb

Var

iabl

esmdash

Inte

rcep

t99

08

534

1

681

3

429

0

710

3

432

3

(82

1)(7

53)

(86

3)(7

73)

(86

0)(8

01)

mdashFe

mal

e11

18

6

72

9

00

5

25

586

3

36

(22

9)(2

11)

(20

0)(1

94)

(19

9)(1

96)

mdashA

ge

ndash38

9

ndash10

4ndash2

51

ndash0

45

ndash16

1

021

(0

44)

(04

0)(0

43)

(03

9)(0

43)

(04

1)mdash

No

Sib

ling

sndash3

25

ndash45

0ndash2

62

ndash45

8ndash2

92

ndash44

7(2

29)

(21

2)(2

03)

(19

6)(1

97)

(19

4)mdash

Res

iden

tial

Sta

bili

tyndash3

20

ndash11

3 ndash2

90

ndash11

1 ndash2

54

ndash09

6 (1

60)

(14

8)(1

39)

(13

4)(1

34)

(13

3)L

agge

d V

aria

bles

tndash1

mdashB

iolo

gica

l Par

ents

mdashmdash

229

2

54

022

1

24

(22

2)(2

11)

(21

7)(2

10)

mdashPa

rent

(s)

on W

elfa

remdash

mdash1

03

022

2

40

092

(2

95)

(27

2)(2

90)

(27

0)mdash

Fam

ily I

ncom

e ($

1000

s)mdash

mdashndash0

01

ndash00

4 0

00

ndash00

3 (0

06)

(00

6)(0

06)

(00

6)mdash

Ris

k P

refe

renc

emdash

mdashndash6

38

ndash6

40

ndash3

68

ndash44

1(2

04)

(18

9)(2

02)

(18

9)mdash

Pri

or P

erce

ived

Ris

k of

Arr

est

mdashmdash

029

017

027

016

(00

4)(0

04)

(00

4)(0

04)

Var

iabl

es a

t Tim

e t

mdashE

xper

ienc

ed C

erta

inty

mdashmdash

gt0

0ndash0

5mdash

mdashmdash

mdashndash1

573

ndash15

46

(3

46)

(32

7)mdash

mdash0

6ndash2

5mdash

mdashmdash

mdashndash8

42

ndash1

156

(33

1)(3

15)

mdashmdash

26ndash

10

mdashmdash

mdashmdash

ndash11

3 ndash4

32

(32

3)(3

02)

(con

tinu

ed o

n ne

xt p

age)


- nullTue 14 Mar 2006 000925



Tab

le 2

(C

onti

nued

)

Mod

el I

Mod

el I

IM

odel

III

The

fta

Vio

lenc

ebT

heft

aV

iole

nceb

The

fta

Vio

lenc

eb

Uns

anct

ione

d O

ffen

ses

mdashmdash

Low

Off

endi

ngmdash

mdashmdash

mdash1

10

ndash22

1 (3

13)

(28

9)mdash

mdashM

ediu

m O

ffen

ding

mdashmdash

mdashmdash

ndash37

6 ndash8

49

(3

21)

(30

0)mdash

mdashH

igh

Off

endi

ngmdash

mdashmdash

mdashndash1

002

ndash10

65

(3

01)

(28

6)mdash

Peer

Vio

lenc

eT

heft

mdashmdash

mdashmdash

ndash79

6

ndash3

00

(1

21)

(10

3)P

seud

o R

20

10

003

0

18

006

0

26

011

u i

SD

301

8

28

42

18

21

21

55

16

43

21

04

ε iS

D41

45

378

6 45

40

403

3 44

99

400

3

Wal

d

211

563

310

7

206

49

74

53

32

748

138

05

(d

f)(1

0)(1

0)(1

6)(1

6)(2

3)(2

3)lo

gndashli

keli

hood

ndash126

314

4 ndash1

3001

83

ndash125

957

8 ndash1

2982

38

ndash125

453

5 ndash1

2953

77

Not

eS

D =

sta

ndar

d de

viat

ion

df

= d

egre

es o

f fr

eedo

m S

tand

ard

erro

rs a

ppea

r in

par

enth

eses

Mod

els

incl

ude

(non

-sig

nifi

cant

) ef

fect

s of

bla

ck H

ispa

nic

con

cent

rate

d di

sadv

an-

tage

pro

port

ion

blac

k c

rim

e ra

te 1

984

and

hig

h im

puls

ivit

ya

N =

32

77

bN

= 3

281

plt

05

plt

01

plt

001

(tw

o-ta

iled

)


- nullTue 14 Mar 2006 000925




A

0

10

20

30

40

50

60

00 gt00-05 06-25 26-100


Per

ceiv

ed R

isk

of A

rres

t (T

heft

)


B

0

10

20

30

40

50

60

00 gt00-05 06-25 26-100


Per

ceiv

ed R

isk

of A

rres

t (V

iole

nce)



- nullTue 14 Mar 2006 000925










- nullTue 14 Mar 2006 000925











Individual

Age

Sex

Race

Family Structure

Impulsivity

Family Income t-1

Risk Preference t-1

Police Contact t-1

Rational Choice

Excitement t-1

Risk of Arrest t-1

Seen as Cool t-1

Employment t-1

Grades t-1

Opportunities t-1

Future Delinquency


Prior Delinquency


Neighborhood


Mobility

Percent Black

Crime Rate



- nullTue 14 Mar 2006 000925














- nullTue 14 Mar 2006 000925














(017) (017) (017) (017) (017) (017)mdashHispanic 022 021 021 024 022 023


(007) (007) (007) (007) (007) (007)mdashCrime Rate 1984 014 012 015 011 015 011










(010) (009) (010) (009)



- nullTue 14 Mar 2006 000925







Table 3 (Continued)








(003) (003)





- nullTue 14 Mar 2006 000925











- nullTue 14 Mar 2006 000925













- nullTue 14 Mar 2006 000925











- nullTue 14 Mar 2006 000925



AAPPPPEENNDDIIXX


Variables











mdashPeer Theftt

mdashPeer Violencet



mdashGradestndash1









mdashViolencet

mdashTheftt

Description





















- nullTue 14 Mar 2006 000925








































- nullTue 14 Mar 2006 000925









































- nullTue 14 Mar 2006 000925





































- nullTue 14 Mar 2006 000925



Tab

le 2

R

ando

m E

ffec

ts T

obit

Mod

els

of P

erce

ived

Cer

tain

ty o

f Arr

est (

Wav

es 3

ndash5)

Mod

el I

Mod

el I

IM

odel

III

The

fta

Vio

lenc

ebT

heft

aV

iole

nceb

The

fta

Vio

lenc

eb

Var

iabl

esmdash

Inte

rcep

t99

08

534

1

681

3

429

0

710

3

432

3

(82

1)(7

53)

(86

3)(7

73)

(86

0)(8

01)

mdashFe

mal

e11

18

6

72

9

00

5

25

586

3

36

(22

9)(2

11)

(20

0)(1

94)

(19

9)(1

96)

mdashA

ge

ndash38

9

ndash10

4ndash2

51

ndash0

45

ndash16

1

021

(0

44)

(04

0)(0

43)

(03

9)(0

43)

(04

1)mdash

No

Sib

ling

sndash3

25

ndash45

0ndash2

62

ndash45

8ndash2

92

ndash44

7(2

29)

(21

2)(2

03)

(19

6)(1

97)

(19

4)mdash

Res

iden

tial

Sta

bili

tyndash3

20

ndash11

3 ndash2

90

ndash11

1 ndash2

54

ndash09

6 (1

60)

(14

8)(1

39)

(13

4)(1

34)

(13

3)L

agge

d V

aria

bles

tndash1

mdashB

iolo

gica

l Par

ents

mdashmdash

229

2

54

022

1

24

(22

2)(2

11)

(21

7)(2

10)

mdashPa

rent

(s)

on W

elfa

remdash

mdash1

03

022

2

40

092

(2

95)

(27

2)(2

90)

(27

0)mdash

Fam

ily I

ncom

e ($

1000

s)mdash

mdashndash0

01

ndash00

4 0

00

ndash00

3 (0

06)

(00

6)(0

06)

(00

6)mdash

Ris

k P

refe

renc

emdash

mdashndash6

38

ndash6

40

ndash3

68

ndash44

1(2

04)

(18

9)(2

02)

(18

9)mdash

Pri

or P

erce

ived

Ris

k of

Arr

est

mdashmdash

029

017

027

016

(00

4)(0

04)

(00

4)(0

04)

Var

iabl

es a

t Tim

e t

mdashE

xper

ienc

ed C

erta

inty

mdashmdash

gt0

0ndash0

5mdash

mdashmdash

mdashndash1

573

ndash15

46

(3

46)

(32

7)mdash

mdash0

6ndash2

5mdash

mdashmdash

mdashndash8

42

ndash1

156

(33

1)(3

15)

mdashmdash

26ndash

10

mdashmdash

mdashmdash

ndash11

3 ndash4

32

(32

3)(3

02)

(con

tinu

ed o

n ne

xt p

age)


- nullTue 14 Mar 2006 000925



Tab

le 2

(C

onti

nued

)

Mod

el I

Mod

el I

IM

odel

III

The

fta

Vio

lenc

ebT

heft

aV

iole

nceb

The

fta

Vio

lenc

eb

Uns

anct

ione

d O

ffen

ses

mdashmdash

Low

Off

endi

ngmdash

mdashmdash

mdash1

10

ndash22

1 (3

13)

(28

9)mdash

mdashM

ediu

m O

ffen

ding

mdashmdash

mdashmdash

ndash37

6 ndash8

49

(3

21)

(30

0)mdash

mdashH

igh

Off

endi

ngmdash

mdashmdash

mdashndash1

002

ndash10

65

(3

01)

(28

6)mdash

Peer

Vio

lenc

eT

heft

mdashmdash

mdashmdash

ndash79

6

ndash3

00

(1

21)

(10

3)P

seud

o R

20

10

003

0

18

006

0

26

011

u i

SD

301

8

28

42

18

21

21

55

16

43

21

04

ε iS

D41

45

378

6 45

40

403

3 44

99

400

3

Wal

d

211

563

310

7

206

49

74

53

32

748

138

05

(d

f)(1

0)(1

0)(1

6)(1

6)(2

3)(2

3)lo

gndashli

keli

hood

ndash126

314

4 ndash1

3001

83

ndash125

957

8 ndash1

2982

38

ndash125

453

5 ndash1

2953

77

Not

eS

D =

sta

ndar

d de

viat

ion

df

= d

egre

es o

f fr

eedo

m S

tand

ard

erro

rs a

ppea

r in

par

enth

eses

Mod

els

incl

ude

(non

-sig

nifi

cant

) ef

fect

s of

bla

ck H

ispa

nic

con

cent

rate

d di

sadv

an-

tage

pro

port

ion

blac

k c

rim

e ra

te 1

984

and

hig

h im

puls

ivit

ya

N =

32

77

bN

= 3

281

plt

05

plt

01

plt

001

(tw

o-ta

iled

)


- nullTue 14 Mar 2006 000925




A

0

10

20

30

40

50

60

00 gt00-05 06-25 26-100


Per

ceiv

ed R

isk

of A

rres

t (T

heft

)


B

0

10

20

30

40

50

60

00 gt00-05 06-25 26-100


Per

ceiv

ed R

isk

of A

rres

t (V

iole

nce)



- nullTue 14 Mar 2006 000925










- nullTue 14 Mar 2006 000925











Individual

Age

Sex

Race

Family Structure

Impulsivity

Family Income t-1

Risk Preference t-1

Police Contact t-1

Rational Choice

Excitement t-1

Risk of Arrest t-1

Seen as Cool t-1

Employment t-1

Grades t-1

Opportunities t-1

Future Delinquency


Prior Delinquency


Neighborhood


Mobility

Percent Black

Crime Rate



- nullTue 14 Mar 2006 000925














- nullTue 14 Mar 2006 000925














(017) (017) (017) (017) (017) (017)mdashHispanic 022 021 021 024 022 023


(007) (007) (007) (007) (007) (007)mdashCrime Rate 1984 014 012 015 011 015 011










(010) (009) (010) (009)



- nullTue 14 Mar 2006 000925







Table 3 (Continued)








(003) (003)





- nullTue 14 Mar 2006 000925











- nullTue 14 Mar 2006 000925













- nullTue 14 Mar 2006 000925











- nullTue 14 Mar 2006 000925



AAPPPPEENNDDIIXX


Variables











mdashPeer Theftt

mdashPeer Violencet



mdashGradestndash1









mdashViolencet

mdashTheftt

Description





















- nullTue 14 Mar 2006 000925








































- nullTue 14 Mar 2006 000925









































- nullTue 14 Mar 2006 000925





































- nullTue 14 Mar 2006 000925



Tab

le 2

(C

onti

nued

)

Mod

el I

Mod

el I

IM

odel

III

The

fta

Vio

lenc

ebT

heft

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77

Not

eS

D =

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ndar

d de

viat

ion

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m S

tand

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ppea

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par

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)


- nullTue 14 Mar 2006 000925




A

0

10

20

30

40

50

60

00 gt00-05 06-25 26-100


Per

ceiv

ed R

isk

of A

rres

t (T

heft

)


B

0

10

20

30

40

50

60

00 gt00-05 06-25 26-100


Per

ceiv

ed R

isk

of A

rres

t (V

iole

nce)



- nullTue 14 Mar 2006 000925










- nullTue 14 Mar 2006 000925











Individual

Age

Sex

Race

Family Structure

Impulsivity

Family Income t-1

Risk Preference t-1

Police Contact t-1

Rational Choice

Excitement t-1

Risk of Arrest t-1

Seen as Cool t-1

Employment t-1

Grades t-1

Opportunities t-1

Future Delinquency


Prior Delinquency


Neighborhood


Mobility

Percent Black

Crime Rate



- nullTue 14 Mar 2006 000925














- nullTue 14 Mar 2006 000925














(017) (017) (017) (017) (017) (017)mdashHispanic 022 021 021 024 022 023


(007) (007) (007) (007) (007) (007)mdashCrime Rate 1984 014 012 015 011 015 011










(010) (009) (010) (009)



- nullTue 14 Mar 2006 000925







Table 3 (Continued)








(003) (003)





- nullTue 14 Mar 2006 000925











- nullTue 14 Mar 2006 000925













- nullTue 14 Mar 2006 000925











- nullTue 14 Mar 2006 000925



AAPPPPEENNDDIIXX


Variables











mdashPeer Theftt

mdashPeer Violencet



mdashGradestndash1









mdashViolencet

mdashTheftt

Description





















- nullTue 14 Mar 2006 000925








































- nullTue 14 Mar 2006 000925









































- nullTue 14 Mar 2006 000925





































- nullTue 14 Mar 2006 000925




A

0

10

20

30

40

50

60

00 gt00-05 06-25 26-100


Per

ceiv

ed R

isk

of A

rres

t (T

heft

)


B

0

10

20

30

40

50

60

00 gt00-05 06-25 26-100


Per

ceiv

ed R

isk

of A

rres

t (V

iole

nce)



- nullTue 14 Mar 2006 000925










- nullTue 14 Mar 2006 000925











Individual

Age

Sex

Race

Family Structure

Impulsivity

Family Income t-1

Risk Preference t-1

Police Contact t-1

Rational Choice

Excitement t-1

Risk of Arrest t-1

Seen as Cool t-1

Employment t-1

Grades t-1

Opportunities t-1

Future Delinquency


Prior Delinquency


Neighborhood


Mobility

Percent Black

Crime Rate



- nullTue 14 Mar 2006 000925














- nullTue 14 Mar 2006 000925














(017) (017) (017) (017) (017) (017)mdashHispanic 022 021 021 024 022 023


(007) (007) (007) (007) (007) (007)mdashCrime Rate 1984 014 012 015 011 015 011










(010) (009) (010) (009)



- nullTue 14 Mar 2006 000925







Table 3 (Continued)








(003) (003)





- nullTue 14 Mar 2006 000925











- nullTue 14 Mar 2006 000925













- nullTue 14 Mar 2006 000925











- nullTue 14 Mar 2006 000925



AAPPPPEENNDDIIXX


Variables











mdashPeer Theftt

mdashPeer Violencet



mdashGradestndash1









mdashViolencet

mdashTheftt

Description





















- nullTue 14 Mar 2006 000925








































- nullTue 14 Mar 2006 000925









































- nullTue 14 Mar 2006 000925





































- nullTue 14 Mar 2006 000925










- nullTue 14 Mar 2006 000925











Individual

Age

Sex

Race

Family Structure

Impulsivity

Family Income t-1

Risk Preference t-1

Police Contact t-1

Rational Choice

Excitement t-1

Risk of Arrest t-1

Seen as Cool t-1

Employment t-1

Grades t-1

Opportunities t-1

Future Delinquency


Prior Delinquency


Neighborhood


Mobility

Percent Black

Crime Rate



- nullTue 14 Mar 2006 000925














- nullTue 14 Mar 2006 000925














(017) (017) (017) (017) (017) (017)mdashHispanic 022 021 021 024 022 023


(007) (007) (007) (007) (007) (007)mdashCrime Rate 1984 014 012 015 011 015 011










(010) (009) (010) (009)



- nullTue 14 Mar 2006 000925







Table 3 (Continued)








(003) (003)





- nullTue 14 Mar 2006 000925











- nullTue 14 Mar 2006 000925













- nullTue 14 Mar 2006 000925











- nullTue 14 Mar 2006 000925



AAPPPPEENNDDIIXX


Variables











mdashPeer Theftt

mdashPeer Violencet



mdashGradestndash1









mdashViolencet

mdashTheftt

Description





















- nullTue 14 Mar 2006 000925








































- nullTue 14 Mar 2006 000925









































- nullTue 14 Mar 2006 000925





































- nullTue 14 Mar 2006 000925











Individual

Age

Sex

Race

Family Structure

Impulsivity

Family Income t-1

Risk Preference t-1

Police Contact t-1

Rational Choice

Excitement t-1

Risk of Arrest t-1

Seen as Cool t-1

Employment t-1

Grades t-1

Opportunities t-1

Future Delinquency


Prior Delinquency


Neighborhood


Mobility

Percent Black

Crime Rate



- nullTue 14 Mar 2006 000925














- nullTue 14 Mar 2006 000925














(017) (017) (017) (017) (017) (017)mdashHispanic 022 021 021 024 022 023


(007) (007) (007) (007) (007) (007)mdashCrime Rate 1984 014 012 015 011 015 011










(010) (009) (010) (009)



- nullTue 14 Mar 2006 000925







Table 3 (Continued)








(003) (003)





- nullTue 14 Mar 2006 000925











- nullTue 14 Mar 2006 000925













- nullTue 14 Mar 2006 000925











- nullTue 14 Mar 2006 000925



AAPPPPEENNDDIIXX


Variables











mdashPeer Theftt

mdashPeer Violencet



mdashGradestndash1









mdashViolencet

mdashTheftt

Description





















- nullTue 14 Mar 2006 000925








































- nullTue 14 Mar 2006 000925









































- nullTue 14 Mar 2006 000925





































- nullTue 14 Mar 2006 000925














- nullTue 14 Mar 2006 000925














(017) (017) (017) (017) (017) (017)mdashHispanic 022 021 021 024 022 023


(007) (007) (007) (007) (007) (007)mdashCrime Rate 1984 014 012 015 011 015 011










(010) (009) (010) (009)



- nullTue 14 Mar 2006 000925







Table 3 (Continued)








(003) (003)





- nullTue 14 Mar 2006 000925











- nullTue 14 Mar 2006 000925













- nullTue 14 Mar 2006 000925











- nullTue 14 Mar 2006 000925



AAPPPPEENNDDIIXX


Variables











mdashPeer Theftt

mdashPeer Violencet



mdashGradestndash1









mdashViolencet

mdashTheftt

Description





















- nullTue 14 Mar 2006 000925








































- nullTue 14 Mar 2006 000925









































- nullTue 14 Mar 2006 000925





































- nullTue 14 Mar 2006 000925














(017) (017) (017) (017) (017) (017)mdashHispanic 022 021 021 024 022 023


(007) (007) (007) (007) (007) (007)mdashCrime Rate 1984 014 012 015 011 015 011










(010) (009) (010) (009)



- nullTue 14 Mar 2006 000925







Table 3 (Continued)








(003) (003)





- nullTue 14 Mar 2006 000925











- nullTue 14 Mar 2006 000925













- nullTue 14 Mar 2006 000925











- nullTue 14 Mar 2006 000925



AAPPPPEENNDDIIXX


Variables











mdashPeer Theftt

mdashPeer Violencet



mdashGradestndash1









mdashViolencet

mdashTheftt

Description





















- nullTue 14 Mar 2006 000925








































- nullTue 14 Mar 2006 000925









































- nullTue 14 Mar 2006 000925





































- nullTue 14 Mar 2006 000925







Table 3 (Continued)








(003) (003)





- nullTue 14 Mar 2006 000925











- nullTue 14 Mar 2006 000925













- nullTue 14 Mar 2006 000925











- nullTue 14 Mar 2006 000925



AAPPPPEENNDDIIXX


Variables











mdashPeer Theftt

mdashPeer Violencet



mdashGradestndash1









mdashViolencet

mdashTheftt

Description





















- nullTue 14 Mar 2006 000925








































- nullTue 14 Mar 2006 000925









































- nullTue 14 Mar 2006 000925





































- nullTue 14 Mar 2006 000925











- nullTue 14 Mar 2006 000925













- nullTue 14 Mar 2006 000925











- nullTue 14 Mar 2006 000925



AAPPPPEENNDDIIXX


Variables











mdashPeer Theftt

mdashPeer Violencet



mdashGradestndash1









mdashViolencet

mdashTheftt

Description





















- nullTue 14 Mar 2006 000925








































- nullTue 14 Mar 2006 000925









































- nullTue 14 Mar 2006 000925





































- nullTue 14 Mar 2006 000925













- nullTue 14 Mar 2006 000925











- nullTue 14 Mar 2006 000925



AAPPPPEENNDDIIXX


Variables











mdashPeer Theftt

mdashPeer Violencet



mdashGradestndash1









mdashViolencet

mdashTheftt

Description





















- nullTue 14 Mar 2006 000925








































- nullTue 14 Mar 2006 000925









































- nullTue 14 Mar 2006 000925





































- nullTue 14 Mar 2006 000925











- nullTue 14 Mar 2006 000925



AAPPPPEENNDDIIXX


Variables











mdashPeer Theftt

mdashPeer Violencet



mdashGradestndash1









mdashViolencet

mdashTheftt

Description





















- nullTue 14 Mar 2006 000925








































- nullTue 14 Mar 2006 000925









































- nullTue 14 Mar 2006 000925





































- nullTue 14 Mar 2006 000925



AAPPPPEENNDDIIXX


Variables











mdashPeer Theftt

mdashPeer Violencet



mdashGradestndash1









mdashViolencet

mdashTheftt

Description





















- nullTue 14 Mar 2006 000925








































- nullTue 14 Mar 2006 000925









































- nullTue 14 Mar 2006 000925





































- nullTue 14 Mar 2006 000925








































- nullTue 14 Mar 2006 000925









































- nullTue 14 Mar 2006 000925





































- nullTue 14 Mar 2006 000925









































- nullTue 14 Mar 2006 000925





































- nullTue 14 Mar 2006 000925





































- nullTue 14 Mar 2006 000925

Documents

Deterring Delinquents: A Rational Choice Model of Theft ...faculty.washington.edu/matsueda/Papers/Deterring.pdf · Deterring Delinquents: A Rational Choice Model of Theft and Violence