Like Godfather, Like Son: Explaining the Intergenerational Nature of Crime*

October 31, 2007

Randi Hjalmarsson† University of Maryland

Matthew J. Lindquist†† Stockholm University

Abstract

This paper provides one of the first in-depth studies of the intergenerational nature of crime and the mechanisms that drive these correlations. We take advantage of a unique Swedish data set, the Stockholm Birth Cohort Study, which contains administrative crime records for multiple generations (fathers and their children). At the extensive margin, we find that sons of fathers with criminal records are 55 percent more likely to have a criminal record themselves than those whose fathers do not have criminal records. At the intensive margin, the son commits, on average, 1.42 more crimes for each additional sentence of the father. Traditional regression techniques indicate that about one-fourth of the association at the extensive margin can be accounted for by common background characteristics, such as socioeconomic status, of both the father and son. Inherited traits, such as ability, account for somewhat less of the association, while the remainder is due to other differences across families, especially parental instability (e.g. mental illness and substance abuse). At the intensive margin, family heterogeneity accounts for an even larger share of the father-son correlation. More direct evidence regarding whether the intergenerational correlations arise through either an inherited traits mechanism or a father as role model mechanism is provided in three alternative experiments. These experiments focus on: (i) a sample of twins, (ii) an adoptee sample, and (iii) the timing of the father’s crime. We find evidence that both direct channels are important for the reproduction of crime from one generation to the next.

* We are especially grateful to Sten-Åke Stenberg. We would also like to thank Kerstin Nelander, Matti Sarvimäki, Jerzy Sarnecki and seminar participants at Gothenburg University, the IZA in Bonn, Stockholm University and Uppsala University. † Contact address for Randi Hjalmarsson: 4131 Van Munching Hall, University of Maryland, School of Public Policy, College Park, Maryland 20742, Office Phone: 301-405-4390, Cell: 203-435-2954, Fax: 301-403-4675, and Email: rhjalmar@umd.edu. †† Contact address for Matthew J. Lindquist: Department of Economics, Stockholm University, SE-106 91 Stockholm, Sweden, Phone: 46+8+163831, Fax: 46+8+159482, and Email: Matthew.Lindquist@ne.su.se.

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1. Introduction

Crime runs in the family. Though this statement may elicit thoughts of the

stereotypes depicted in movies like The Godfather or television series like The Sopranos,

this stylized fact actually has a long history in criminology.1 There is also substantial

anecdotal evidence of the familial nature of crime. For example, The New York Times

published an article entitled, “Father Steals Best: Crime in an American Family” (Fox

Butterfield, August 21, 2002) about a family of criminals in Oregon. This particular

article received attention throughout the United States and has been cited by politicians,

law enforcement agencies, and researchers in criminology, psychology and, now,

economics.

Butterfield’s article describes how the Bogle family patriarch, Dale Vincent

“Rooster” Bogle, taught his children to steal, so that by age 10 his sons were already

breaking into liquor stores or stealing tractor-trailer trucks. His daughters turned to petty

crimes in order to support their drug addictions. By the time of his death in 1998, 28 of

the Bogle clan had been convicted of crimes, including several of Rooster’s

grandchildren. Tracey Bogle, the youngest of Rooster’s sons, is quoted as saying,

“Rooster raised us to be outlaws…There is a domino effect in a family like ours…What

you’re raised with, you grow to become. You don’t escape.”

Although the Bogle family may be an extreme example (and the Corleone and

Soprano families purely fictitious), Justice Department figures (cited by Butterfield)

show that 47 percent of inmates in state prisons have parents or other close relatives who

have also been incarcerated. The cost of ignoring this family cycle of criminality may be 1 See Rowe and Farrington (1997) and the historical references therein.

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huge. Butterfield cites an official of the Oregon Department of Corrections who

estimated the cost of incarcerating just five of the convicted Bogle family members at

nearly three million dollars (in 2002 prices). And, this may just be a lower bound given

that it neither accounts for the many administrative costs of their trials, probation, or

paroles nor the costs born by the victims of their crimes.

It is therefore surprising that little in-depth research has been conducted regarding

the relationship between an individual’s own criminality and that of his parents.2 This

omission, we believe, is largely due to the limited availability of data that contains the

criminal records of multiple generations. The lack of research on the intergenerational

nature of crime can be considered a gap in two lines of research. First, it represents an

important gap in the crime literature, as there are substantive policy implications of

intergenerational criminal correlations. Such a relationship could imply that policies that

appear to be successful at reducing crime today may reduce crime even further in the

future. Thus, research may underestimate the effects of policies that treat or deter

criminal behavior by not taking into account the effect on future generations. Similarly,

estimates of the savings to society of crime reducing policies will also be understated if

the effect extends to the next generation.3

2 Rather, when studying the determinants of crime, economists, for instance, tend to emphasize those factors illuminated in Becker’s (1968) economic model of crime, e.g. the probability and severity of punishment as well as the returns to legitimate employment. 3 After rising for many years, crime rates in the U.S. reached a peak in the early to mid 1990s. This trend was observed for both property and violent crimes. By 2004, the property crime rate was about 30 percent lower than in 1991 and the violent crime rate was almost 40 percent lower. Many factors have been considered as the sources of these declines, including more police (Levitt, 1997), more incarceration (Levitt, 1996), legalization of abortion (Levitt and Donohue, 2001), and even decreases in childhood lead exposure (Reyes, 2007). One question not asked, however, is whether this decrease in crime will be reflected in the crime patterns of the next generation. If there is an intergenerational component to crime, then one may in fact expect this to be the case.

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Second, the minimal research on intergenerational criminal correlations can also

be seen as a gap in the labor market literature, which has extensively studied

intergenerational mobility. This literature asks how much one’s relative position in the

earnings distribution depends on the position of one’s parents in society. While the focus

of this literature has been on intergenerational income mobility, other outcomes, such as

career mobility, welfare dependence and education, have also been studied.4 If one

considers participation in criminal activities to be participation in the ‘illegitimate’ labor

market, then previous research, which studies intergenerational mobility in legitimate

labor market earnings, has omitted to study a segment of the labor market that may be of

particular importance to children of low income parents. Thus, studying

intergenerational criminal correlations is a natural extension and an important component

of the intergenerational income mobility literature.

This paper addresses the gaps in both of these literatures by taking advantage of a

Swedish dataset, the Stockholm Birth Cohort Study (SBC), which contains administrative

crime records for both a cohort of more than 15,000 individuals born in 1953 (and

residing in Stockholm in 1963) and their fathers. Due to the combination of a relatively

large sample size, detailed criminal and non-criminal measures, as well as a long time

horizon, this Swedish data set provides a unique opportunity to study intergenerational

criminal correlations. Specifically, we will study whether the criminal outcomes of

fathers and their children are correlated as well as the factors or mechanisms driving

these relationships.

4 Solon (1999) provides an excellent review of the intergenerational mobility labor market literature. See Breen and Jonsson (2005) for a review of the sociology literature on educational and occupational mobility.

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What research has been done to date clearly motivates the need for further study.

For instance, Glueck and Glueck (1950) study a sample of just 1,000 American boys, half

of whom are identified as delinquents; they find that 66 percent of delinquents had a

criminal father while just 32 percent of non-delinquents had a criminal father. Using the

1958 Philadelphia Birth Cohort Study, Williams and Sickles (2002) find that 15 percent

of criminals report having a father who was arrested during the respondent’s childhood

while just 6 percent of non-criminals report that their father was arrested. A handful of

British studies (Ferguson, 1952; Wilson, 1987; and Farrington and West, 1990) also

suggest that an individual’s criminality is related to that of his family members. Using

the Cambridge Study on Delinquent Development, Farrington and West (1990) find that

75 percent of a sample of dual conviction couples produced offspring who were also

convicted. Gregory (2004) provides a brief review of these studies as well as those that

consider the relationship between individual criminality and non-crime family

characteristics.

Janson (1982) uses the SBC data to investigate criminality among boys. He

tabulates the delinquent behavior of boys younger than 15 years old by both social class

and fathers’ criminality. In total, 9.8 percent of the boys with fathers that have no

criminal record are delinquent, while 19.9 percent of the boys with criminal fathers are

delinquent. This positive correlation exists even within different social classes. Similar

results are obtained for delinquent boys between the ages of 15 and 18; 25.4 percent of

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the boys with criminal fathers are delinquent, while 12.6 percent of the boys without

criminal father are delinquent.5

Though these studies provide some evidence of intergenerational criminal

correlations, none identify why this relationship exists.6 That is, is it due to the existence

of some common external factor or is there a mechanism that links parent criminality to

child criminality? Is the link between father and child criminality due to nature or

nurture? In addition, the focus of these studies has been the extensive margin, i.e. whether

any crimes are committed, as opposed to the intensive margin, i.e. the number of crimes,

or the types of crimes. Thus, the current study will also extend the literature in these

directions.

Our study is carried out in three stages. First, we document the existence of

significant intergenerational correlations in criminal outcomes. In particular, we look at a

father’s sentencing record and correlate it with his son’s or daughter’s criminal record. In

the second stage, we try to explain this intergenerational correlation using traditional

regression techniques. We find that sons of fathers with criminal records are 55 percent

more likely to have a criminal record themselves than those whose fathers do not have

criminal records and that the son commits, on average, 1.42 more crimes for each

additional sentence of the father. Including a large set of controls, we can account for

nearly all of this observed association. At the extensive margin, common background

variables, such as socio-economic status, account for roughly one-fourth of the

association. Inherited traits, such as ability, account for somewhat less of the association.

5 In a more recent study, Murray et al. (2007) used the SBC data to examine the effect of parental incarceration on children’s criminal behavior. They find a strong intergenerational correlation in criminality, but no extra effect of parent’s time in prison on children’s offending. 6 Rowe and Farrington (1997) study the familial transmission mechanism. But their results are quite ambiguous. They do, however, report evidence of a very interesting socialization effect among siblings.

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The remainder is due to family heterogeneity, in general, and parental instability (e.g.

mental illness and substance abuse), in particular. Unobserved neighborhood

characteristics (captured with neighborhood fixed effects) explain little to none of the

intergenerational criminal relationship. At the intensive margin, family heterogeneity

plays an even larger role in accounting for the father-child association.

In the third stage of our analysis, we present more direct evidence of a causal link

between fathers’ criminality and that of their children by running a series of alternative

experiments. Such experiments can help shed some light on the mechanisms underlying

these correlations. For instance, Mednick, Gabrielli, and Hutchings (1984) compare the

court convictions of a sample of more than 14,000 adoptees to court convictions of their

biological and adoptive parents. Their results support a story of nature rather than

nurture, as a significant relationship is found between the adoptees and their biological

parents but not between the adoptees and their adoptive parents. Our experiments focus

on: (i) the sample of twin pairs in our data as well as knowledge about their zygoticity,

(ii) children who were adopted at a young age, and (iii) the timing of the father’s crime.

Through these experiments, we consider two direct channels: (i) inherited traits and (ii)

the father as a role model. We find evidence that both of these direct channels play a role

in the reproduction of crime from one generation to the next.

The remainder of this paper is organized as follows. Section 2 describes the data

and Section 3 presents a full set of intergenerational criminal correlations. Sections 4 and

5 present the second and third stages of the analysis described above. In Section 6, we

discuss the external validity (generalizability) of our results. We also replicate several

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intergenerational criminal correlations using what little U.S. data is available. Section 8

summarizes our findings and discusses policy implications.

2. Data

Crime Rates in Sweden, Europe and the U.S.

Before presenting our data, we must first put one popular misconception to rest.

Sweden is not a country free from crime. A recent victimization study conducted by the

EU ICS (2005, p. 97) concluded that: “In the EU context the level of crime in Sweden is

medium high. Exposure to drugs-related problems is relatively rare. The prevalence of

hate crimes is just above the EU mean.” Official crime statistics for murder and motor

vehicle thefts, arguably the two best documented and most comparable crimes across

counties, are presented in Table 1. While murder is fairly rare in Sweden, motor vehicle

theft is extremely common; in fact, the motor vehicle theft rate is twice as high in

Sweden as in the United States.

Description of the Data Set

The data set used in this paper is the Stockholm Birth Cohort Study (SBC), which

is a database that was created in 2004 by matching two data sets.7 The first data set, the

Stockholm Metropolitan Study (SMS), consists of all children born in 1953 and living in

the Stockholm Metropolitan area on November 1, 1963. Data was collected from multiple

sources, including individual and family surveys as well as administrative records; thus,

7 Carl-Gunnar Jansson and Sten-Åke Stenberg managed and provided the original cohort data, Denny Vågerö organised the follow-up data and Reidar Österman managed the probability matching of the two data sets. Preparing data from the Stockholm Birth Cohort Study is an ongoing collaborative effort by the Swedish Institute for Social Research and by Centre for Health Equity Studies, partly financed by the Swedish Research Council. For a complete description of the project and data set see Stenberg et al. (2007).

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this data set contains a rich set of variables concerning individual, family, social, and

neighborhood characteristics. The SMS data was matched to The Swedish Work and

Mortality Database (WMD), which consists of income, work, unemployment, in-patient,

and mortality data for all individuals living in Sweden in 1980 or 1990 and who were

born before 1985.

The work in this paper is based on data originating from the Stockholm

Metropolitan Study, which consists of 15,117 individuals: 7,719 men and 7,398 women.

The most important feature of this data is that it contains crime records of both the birth

cohort and their fathers. For the birth cohort, we have measures of criminality for two

time periods. Data for when the individual is below the age of 18 is primarily sourced

from the child welfare committees (CWC).8 Records from the CWC are not available,

however, for individuals who have moved out of the Stockholm area by this time;

approximately 950 individuals moved out of the Stockholm area between the ages of 13

and 18. For individuals who remained in the area, we can identify the following

information about their juvenile criminal/delinquent activities: the number of

interventions made by the CWC, the number of such interventions which resulted in time

in foster care or in an institution, as well as the general type of delinquent behavior

(stealing, violent crimes, alcohol abuse or narcotics, and other offenses).

As the birth cohort ages into adulthood, crime data is obtained from the official

police registrar (person- och belastningsregister, PBR).9 This registrar contains records of

offenses that lead to an official report to the CWC or to a conviction. These crime data

8 As in the United States, juveniles in Sweden are not subject to the same punishments as adults and are not processed by the same authorities. 9 Since the PBR is a national register, we do not lose cohort members that move out of the Stockholm metropolitan area.

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are divided into seven crime categories, including: violent crime or crimes against

persons, stealing, fraud, vandalism, traffic crimes (that lead to a court conviction, e.g.,

driving without a license or under the influence of alcohol), narcotic crimes, and other

crimes. For each year from 1966 to the first half of 1984 (i.e. when the respondents are

age 13 through 31), we have information on the number of offenses in each of these

crime categories as well as the sentence that was received; the 1966 data is actually a

summary of all known crimes reported up to and including 1966.

The fathers’ crime data also comes from the official police registrar. For the

fathers, we can identify the number of times that he appears in the registrar and his

sentences for three time periods: pre-1953 (before the birth of the cohort member), 1953-

1959 (until the cohort member is six) and 1959 – 1972 (until the cohort member is 19).

Possible sentences are probation, which includes a fine, imprisonment, and exemption

from punishment due to psychiatric care or alcohol treatment. One limitation of the data

is that we only know whether the father was sentenced to prison but not the actual length

of time in prison. This limits our ability to characterize the degree or severity of the

father’s criminal behaviors. However, we can still characterize incarcerated fathers as

more severe criminals than fathers sentenced to punishments other than incarceration or

fathers with multiple sentences as more criminal than fathers with just one sentence, etc.

We can also identify whether the father was charged with drunk or dangerous driving

during each of these time periods.

Lastly, we will use an extensive set of controls in this study to help disentangle

the source of the intergenerational criminal relationship. Registry data is available for a

wide range of variables characterizing social background, including: residential district,

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social class, nationality, family composition, marital status, employment, means tested

social assistance, income, and education. We will also use a number of variables to

control for inherent ability, including: a sixth grade test of intelligence, a second test of

intelligence (for males) taken at the start of compulsory military service, and school

grades.

Descriptive Statistics

Table 2 presents variable definitions and summary statistics for the cohort

member and father crime variables, broken down by sons and daughters. Not

surprisingly, females commit much fewer crimes than males. 20 percent of the male

cohort members have a juvenile record while just 6 percent of the girls do. Similarly, 33

percent of males have a record as an adult while just 7 percent of females do.10 The

higher propensity of males to commit crime is also seen when examining participation in

particular crime categories, both as a youth and as an adult. The most common crime for

both males and females is theft; 19 percent of males and 4 percent of females have

committed a theft. The average number of crimes committed by males in the sample is

3.4 while the female average is less than one half. Again, theft is the dominating crime

10 The Cambridge Study in Delinquent Development follows a sample of London boys also born in 1953. Using this data, Farrington and Wikström (1994) show that the cumulative prevalence of offending between the ages of 10 and 25 are almost identical for the London boys and the Stockholm boys that we study. By age 25, 32.1 percent of the Stockholm boys and 32.6 percent of the London boys had criminal records. Tillman (1987) reports that 33 percent of all men born in 1956 and living in California at the time of his study had been arrested at least once between the ages of 18 and 30. Wolfgang, Figlio and Sellin (1972) report that 35 percent of all Philadelphia men born in 1945 had been arrested by the age of 18. For Denmark, Damm and Dustmann (2007) report that 31 percent of their post-reform, male refugees have been convicted of a crime by age 21. Anna Piil Damm was also kind enough to calculate for us that 25 percent of all Danish men have a criminal record by age 30. And although we don’t have the exact number in hand, the rate for Copenhagen men is bound to be higher (i.e. closer to the Stockholm rate). We feel that these nearly identical cumulative offending rates across cohorts increases the likely generalizability of our study since it shows that the Stockholm birth cohort offends at the same rate as other cohorts from other metropolitan areas and from other Western countries.

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category when considering this intensive margin. About 12 percent of the fathers have at

least one sentence on their record.11 Probation is the most common sentence: 8 percent

have a probationary sentence, 3 percent have a prison sentence, 4 percent have a drunken

driving sentence, and just one percent receives an exempt sentence.

Table 3 takes a preliminary look at whether children with criminal tendencies are

more likely to come from criminal fathers. It tells us that male cohort members whose

fathers have criminal records are 55 percent (17.2 percentage points) more likely to have

adult criminal records and commit, on average, 3.77 more crimes than those with fathers

without criminal records. Female cohort members whose fathers have criminal records

are 142 percent (8.4 percentage points) more likely to have an adult criminal record and

commit more than three times as many crimes, on average, as those with fathers without

a criminal record.

3. Intergenerational Criminal Correlations

We begin our empirical investigation by examining a set of raw correlations

between the criminality of our cohort members and the criminality of their fathers. These

correlations are reported separately for sons and daughters.12 Each table reports a set of

11 Williams and Sickles (2002) report that 7 percent of the fathers in their sample from the Philadelphia Birth Cohort Study had been arrested at least once during the cohort member’s childhood. These data are reported by the son (the cohort member) about his father, which may bias the number downwards since children may not recall or have full information on their fathers’ arrest records. Also, the question is formulated so as to exclude any arrests prior to the cohort member’s childhood. In Rowe and Farrington’s (1997) analysis using a sample of London boys from the Cambridge Study in Delinquent Development, 28 percent of the fathers have a criminal conviction. These data are taken from official police registers, but the fathers are not a representative sample. Nearly all of these fathers (about 94 percent) had a working-class occupation (skilled, semiskilled, or unskilled manual worker). If we look at fathers with a similar socio-economic status in 1963 using the SBC data, we find that 18 percent have been convicted of a crime. 12 We distinguish between sons and daughters because there are large differences in the number and types of crimes committed by men and women in our sample. The father-child mechanism may differ for different types of crimes or by the gender of the child. We do not want to loose these distinctions by pooling men and women together.

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40 correlations, which correspond to the seven categories of crime available for cohort

members plus an eighth category that simply tells us that they appear in the official police

registrar together with the four sentencing categories that we have for each father plus a

fifth that indicates if the father has been convicted at least once. We first report

correlations at the extensive margin. Has the father been sentenced for at least one crime,

yes or no? Has the cohort member been convicted of at least one crime, yes or no? This is

followed by a set of correlations at the intensive margin, which tells us whether the

number of sentences of a father is correlated to the number of crimes committed by his

son or daughter. Lastly, we examine correlations between fathers’ criminality and their

children’s delinquent behavior when young in order to see if “criminal” correlations

appear early or late in the life of the cohort member.

Father-Son Correlations at the Extensive Margin

Father-son criminal correlations at the extensive margin are reported in Table 4.

Our main finding is that there are significant correlations between the criminality of

fathers and their sons. If we focus our attention on the first row of Table 4, we see that

the “main” correlation, Father – Crime, is equal to 0.118 (0.0116).13 The correlations

between Father – Violent and Father – Steal are equally large, 0.103 (0.0141) and 0.123

(0.0131), respectively.

The fact that we are dealing with a dichotomous variable that is not equally

distributed between fathers and sons implies that the maximum value that this correlation

can take on is less than one. For example, we have 918 criminal fathers who have sons.

Out of all of the sons in the cohort, 2567 have criminal records. The maximum possible 13 Numbers in parentheses are bootstrapped standard errors.

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correlation can be found by assigning each criminal father to a criminal son and then

calculating the correlation (i.e. we simulate an artificial data set and use it to measure the

highest possible correlation given actual sample characteristics). In this case, the

maximum correlation is 0.52 (0.0082). Thus, the size of our main father-son criminal

correlation, 0.118 (0.0116), should be judged in reference to the fact that such a

correlation can take on a maximum value of 0.52.

Is 0.118/0.52 = 0.23 a large correlation or a small one? In Table 3, we saw that

this correlation raises a son’s probability of conviction by 17.2 percentage points (55

percent). This is equivalent to a log-odds ratio of 2.06, which implies that sons with

convicted fathers are more than twice as likely to be convicted as sons whose fathers

have not been convicted. Another way of putting the magnitude of the father – son

criminal correlation into perspective, is by comparing it to a more familiar quantity,

namely the father – son correlation in earnings, which is of the same magnitude as our

main father – son criminal correlation.14

Father-Daughter Correlations at the Extensive Margin

A full set of father-daughter criminal correlations at the extensive margin is

reported in Table 5. Our “main” correlation (Father – Crime, shown in the first column of

14 Using a single-year measure of log labor earnings produces a correlation of 0.105 (0.0144) in our sample. The father – son earnings elasticity is 0.153 (0.0203). Using a different sample from Stockholm, Sweden, Gustafsson (1994) reports a father – son elasticity in income of 0.14. Both of these elasticities, however, are biased downward by their use of single-years measures of earnings or income by a factor around 1.4 to 2 (Solon, 1999). Multiplying our father – son earnings estimates by this adjustment factor gives us a father – son elasticity in log earnings of around 0.21 to 0.31, which are similar to the unbiased estimate of 0.28 reported in Björklund and Jäntti (1997). Thus, the father – son correlation in crime is about half as large as that previously found for labor earnings. If one takes into account the fact the maximum possible criminal correlation is 0.52 (not 1), then our estimates could be considered roughly equal to those found for earnings.

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the first row) is equal to 0.110 (0.0143).15,16 It is statistically significant and roughly

equal to the corresponding father-son correlation, 0.118 (0.0116). There are, however, a

number of important differences between the father-daughter and father-son correlations.

Only 29 out of the 40 father-daughter correlations are statistically different from zero,

while all but five of the father-son correlations are significant. Most of the father-

daughter coefficients are smaller than their father-son counterparts; nine are significantly

smaller. In particular, all of the father-son correlations associated with violent crimes,

stealing and vandalism are larger than the corresponding father-daughter correlation.

However, four out of five correlations concerning narcotics crimes are, in fact, larger

between fathers and daughters, one significantly so.

Father-Son Correlations at the Intensive Margin

Now we want to investigate whether the number of sentences received by a father

is correlated with the number of crimes committed by the son. Table 6 presents a full set

of father-son criminal correlations at the intensive margin. It is hard to distinguish any

clear pattern in the differences between these correlations and the extensive correlations

reported in Table 4. The “main” correlation between fathers’ and sons’ criminality is

equal to 0.101 (0.0202), which is close to its extensive counterpart. Of the 40 intensive

correlations, 22 are lower than their extensive counterparts, while 18 are higher. There

are now seven zero correlations as opposed to five in Table 4. The only pattern that

seems to emerge is that nearly all of the intensive correlations concerning fathers’ drunk

15 An upper bound on the father-daughter correlation is equal to 0.715 (0.0130). 16 In Table 3, we saw that this correlation raises a daughter’s probability of conviction by 8.4 percentage points (142 percent). This is equivalent to a log-odds ratio of 2.66.

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or dangerous driving and sons’ serious traffic violations are larger than their extensive

counterparts (compare column 6 and row 4 in Table 6 to those found in Table 4).17

Father-Daughter Correlations at the Intensive Margin

Table 7 presents the father-daughter correlations at the intensive margin. In

general, the father-daughter intensive correlations tend to be weaker than their extensive

counterparts. There are now 24 zero correlations (as opposed to 11 in Table 5) and most

of the correlations (32 out of 40) are smaller in size than their extensive counterparts; 11

of the father-daughter intensive correlations are significantly smaller than their father-son

counterparts, while only 1 is significantly larger.18

Correlations between Fathers’ Criminality and Juveniles’ Delinquent Behavior

Lastly, we investigate whether these types of correlations appear early or late in

life by constructing a set of correlations at the extensive margin between fathers’

criminality and their children’s delinquent and/or criminal behavior when young. Here

we use information taken from the files of the child welfare committees. In Table 8, we

see that our “main” correlations for cohort members aged 7 to 12 are significant, but

much lower than their adult counterparts in Table 4 and Table 5. Furthermore, we see that

all of these correlations rise significantly with age except for the correlation concerning

17 The number of sentences served by the father is not an unproblematic measure of the intensity of criminal behavior of that father. One very long sentence may include many (e.g. 10 or more) serious crimes, while two short sentences may only represent two crimes. This measure of intensity is then correlated with the number of crimes committed by the son, which is our preferred measure of intensity. As a robustness check, we have also correlated the number of fathers’ sentences with the number of sons’ sentences. This correlation is 0.114 (0.0181), which is slightly higher than the correlation reported in Table 6, but not significantly so. 18 We have also correlated the number of fathers’ sentences with the number of daughters’ sentences (as opposed to crimes). This correlation, 0.106 (0.0212), is higher than the correlation reported in Table 7, but not significantly so.

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daughters’ violent crimes, which falls to the same level found among adult women. In all

cases, the correlations for children aged 13 to 19 are roughly equal to those found for

adult cohort members. Thus, the father-child criminal correlations reported in Table 4

through Table 7 begin to appear between the ages of 7 and 12 and are fully established

between the ages of 13 and 19.19 This finding implies that we should not be looking at

actual adult outcomes of cohort members in order to explain the father-child correlation.

However, expectations (when young) about the future (when adult) can be used as

arguments.

Summary of Correlation Results

The findings from our initial investigation of the father – child criminal

correlations can be summarized as follows. (1) The main father – son correlation at the

extensive margin is 0.12. (2) Father – son crime is correlated across all types of crime.

(3) There are no clear differences between intensive and extensive margin correlations,

except between fathers’ and sons’ drunk or dangerous driving. There is a stronger father

– son correlation for repeat offenders. (4) The main father – daughter criminal

correlation at the extensive margin is 0.11. (5) Most of the father – daughter correlations

are smaller than their father – son counterparts. (6) The father daughter intensive

correlations are weaker than the extensive correlations. (7) Father – child correlations in

crime appear between the ages of 7 and 12 and are fully established by age 19.

19 There is significant overlap between the CWC data and the PBR data for those aged 13 to 19. Crime in the SBC data peaks at age 17. Stealing is by far the most prevalent crime at this age. Of those who commit crimes, 12 percent make their debut by age 13. By age 19, 58 percent have made their debut. In the United States, arrest rates peak between age 16 and 18 (Blumstein, 1995).

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4. Explaining Father-Child Criminal Correlations: An Initial Regression Analysis

Regression Methodology

The second aim of our empirical analysis is to understand why such a relationship

exists and, in particular, whether it is causal or can simply be attributed to the fact that

both the father and child come from similar social backgrounds. As is the case when

studying income mobility, there are a number of mechanisms through which

intergenerational correlations in criminality may arise. These can broadly be categorized

in terms of nature versus nurture. We will analyze those factors underlying the father-son

crime relationship by regressing a measure of the son’s criminality, Cs, on a measure of

the father’s criminality, Cf, as depicted in equation (1) and add in four vectors of controls

in a stepwise fashion.

(1) s f sf s family n sfnC C SES A Xα β γ δ λ µ ε= + + + + + +

The first vector of controls, SESsf, captures the father’s socioeconomic status and includes

such traditional variables as the father’s social class in 1953 and 1963, immigration

status, the employment status of the household head in 1960 (typically the father), and the

father’s income in 1963. The second vector of controls, As, proxies for the son’s ability

and includes IQ test scores from the sixth grade and the military draft as well as the

cohort member’s grades from sixth grade. Inherited ability, or a lack thereof, is one

example of nature being the driving force underlying the intergenerational crime

relationship. It is important to recognize, however, the difficulties inherent in isolating

the role played by inherited ability. For instance, traditional measures of ability, such as

18

test scores and especially grades, are potentially influenced by social background factors.

For this reason, we first control for socioeconomic status and then for ability, thereby

presenting fairly conservative estimates of the role played by inherited ability.20

The next vector of controls, Xfamily, includes variables that characterize potentially

important characteristics of both the mother and father. Included in this vector are

measures of: (i) household means-tested social support21, (ii) mental health for the mother

and father, (iii) alcohol abuse of the mother and father, (iv) parental deaths and (v)

attitudes, typically reported by the mother. The last category includes variables that

measure whether the cohort member was allowed to be absent from school when not sick

and allowed to smoke. These five categories of variables are included as an attempt to

control for the otherwise unobserved heterogeneity across families with the same

socioeconomic status. Not all children in low income families grow up to be criminals

and not all children in well-off families are non-criminals. There must be some child- or

family-specific shock or heterogeneity that produces criminals; it is this shock or

heterogeneity that the vector of family variables attempts to capture.

Lastly, we include neighborhood fixed effects, µn, where the neighborhood is

defined as the cohort member’s district of residence in 1963. Cohort members lived in

more than 600 such neighborhoods at age 10. However, they are not evenly distributed

20 Of course, it is also possible that the father’s socioeconomic status is partly driven by his ability. 21 It is important to note that, unlike welfare in the United States, means-tested social support in Sweden is not primarily aimed at single-mothers. In Sweden, all single-parents receive support through a system of family support that is not means-tested. Furthermore, we could have included means-tested social support as an indicator of socio-economic status. But we believe that it is a better signal of family heterogeneity than of long-run socio-economic status. Our reasoning follows that of Sten-Åke Stenberg (2000) who has studied the inheritance of welfare recipiency between generations using the SBC data. He argues that, “Because the main part of Swedish social benefits is universal, families who fall through this economic safety net and must rely on means-tested assistance as their last resort are likely to be a more negatively selected group with respect to different types of social problems. Therefore, we could expect to find a greater representation of non-economic problems here than among welfare families in the United States.” (p. 233).

19

across these neighborhoods; 140 neighborhoods include just one cohort member. The

mean number of cohort members form the same neighborhood is about 23. Such fixed

effects could potentially capture information about the community’s economic status,

criminality, schools, peers, etc. Clearly, neighborhood characteristics such as these may

help explain an individual’s propensity to engage in crime. But, there are also a number

of reasons why such characteristics can also potentially explain some of the father – child

crime relationship. As is the case for socioeconomic status, one possibility is that the

neighborhood characteristics are common to both the father and the son. However, it is

also possible that the neighborhood in which a child lives is chosen by his father and that

this choice may not be exogenous to the father’s criminality. For instance, the father may

choose to live in a neighborhood where his criminal record has less of a stigma.

Alternatively, it may be that the father’s criminal record limits the set of neighborhoods

from which he can choose to live in. Therefore, neighborhood fixed effects may also

account for some of the father – child crime relationship as a result of the father’s

criminality indirectly affecting the neighborhood in which the child grows up. 22

Extensive and Intensive Margin Results

Table 9 presents the marginal effects that result from estimating equation (1) for

sons with a probit model; note that all extensive margin specifications are estimated in

this way. Both father and son criminality is measured at the extensive margin; for the

father, we are considering whether or not he has a record prior to 1972 and for sons, we

are considering whether they have an adult record through the first half of 1984. Males

22 Parallel regression analyses are also conducted for daughters, the only difference being that draft test score data does not exist for females.

20

in the cohort who have fathers with a criminal record are, on average, 55 percent (17.2

percentage points) more likely to have a criminal record themselves. Column (2) of

Table 9 adds in the vector of socioeconomic status controls, reducing the father-son

criminal correlation by 26 percent; the point estimate decreases from 0.172 to 0.127.

Column (3) adds in the ability vector and further decreases the coefficient on father

criminality to 0.100. That is, over and above controls for socioeconomic status, proxies

for ability account for approximately 16 percent of the raw father-son criminal

correlation. Given that socioeconomic status could to some extent also proxy for ability,

this is a conservative estimate of the role of ability; at the other end of the spectrum, we

find that ability accounts for 32 percent of the father-son relationship when it is allowed

to enter the regression analysis prior to socioeconomic status.

Column (4) adds in controls for whether the cohort member’s family received any

social support from 1953 to 1972 as well as the number of years of support. Whether or

not the family receives social support accounts for a large portion of the father – son

crime relationship; the coefficient has now been driven to 0.058. There are a number of

mechanisms that can potentially explain this. One possibility is that chronic poverty

causes crime, for both fathers and sons. If this is the case, then it would point towards

common factors accounting for much of the correlation. Another possibility is that the

household is receiving social support because the father is not around. Alternatively, the

father may be around but have minimal legal income, resulting in the authorities having

to support the family. Whatever the underlying mechanism, however, it is clear that the

prolonged use of social support during this historical time period may be a signal of

parent type; note that it is not just poor, single mothers who receive social support but,

21

rather, all types of families. Given its apparent importance, we will return later to the role

of social support. Column (5) adds in the vector of non-traditional family characteristics;

the point estimate on father criminality decreases from 0.058 to 0.049 and remains

significant. Thus, even with the complete set of observable controls, there is still some

portion of the father – son crime relationship that is left unaccounted for: sons with

criminal fathers are still 4.9 percentage points more likely to have a record themselves.

Column (6) adds in neighborhood fixed effects, but this does not further explain the

father – son relationship.

Thus far, the regression analysis only considers intergenerational criminal

correlations at the extensive margin. However, this does not allow us to distinguish one

time offenders from chronic offenders and to assess whether the mechanisms underlying

these intergenerational correlations differ for these two classes of offenders. Thus, Table

10 presents the OLS results of estimating equation (1) for sons, when considering the

number of crimes committed by the fathers and their children. The first column of Table

10 indicates that for each additional sentence of the father, sons commit 1.4 more

offenses on average. Column (2) adds in the socioeconomic status controls, reducing the

coefficient on the number of father sentences to 1.2 or by approximately 15 percent. In

contrast, at the extensive margin, controls for socioeconomic status accounted for 26

percent of the father-son relationship. Similarly, the role of ability appears to be smaller

at the intensive than the extensive margin. When controlling for the vector of ability

proxies, the coefficient on father criminality decreases to 1.07; ability only accounts for

10 percent of the raw father-son relationship. (Even when ability is entered prior to

socioeconomic status, it only accounts for 17 percent of the relationship.) Once again,

22

however, controlling for social support accounts for a significant portion of the father-son

relationship; as seen in column (4), the coefficient is cut almost in half to 0.570 and

becomes insignificant. The remaining family characteristics further reduce the father-son

relationship, though it remains insignificant. As at the extensive margin, neighborhood

fixed effects in column (6) do not help to further explain the intergenerational

relationship. Therefore, because these neighborhood effects do not seem important in the

current context and because they are asking a lot of the data, we exclude them from the

remainder of the analysis.

To summarize, Table 9 and Table 10 indicate that both social background factors

and inherited ability play potentially important roles in explaining the father-son criminal

correlations. Ability likely explains between 16 and 32 percent of this relationship at the

extensive margin and 10 to 17 percent at the intensive margin. It is also clear that much

heterogeneity exists across households even after controlling for the vector of

socioeconomic status variables. Most importantly, whether or not the family received

social support accounts for a large portion of these intergenerational correlations. The

mechanism underlying this, however, has not yet been identified. Despite the large set of

controls, a portion of the father – son relationship is still left unaccounted for.23

Explaining the Relationship between Father and Child Criminality by Type of Crime

23 Father-daughter regressions at both the extensive and intensive margins were also conducted but are not presented as the results yield a qualitatively similar picture. Females with criminal fathers are, on average, 142 percent (8.4 percentage points) more likely to become criminals themselves. At the intensive margin, for each additional sentence of the father, the daughter commits, on average, 0.25 more offenses. Our results are similar to those of Moffitt et al. (2001) who found that the same set of risk factors predicted antisocial behavior in both males and females. They argue that the large difference in the level of antisocial behavior between males and females is largely due to the fact that males have a higher exposure to these risk factors.

23

The results presented above have aggregated all types of crimes together. Table

11 explores whether the intergenerational correlations, at both the extensive and intensive

margins, differ when looking at various categories of crime. At the extensive margin for

sons, we see the raw relationship ranging in strength from theft (0.15) and traffic offenses

(0.09) to narcotics (0.04). Controlling for both socioeconomic status and ability decrease

the father – son relationship between 46 and 61 percent, depending on the crime

category; the largest impact is for narcotics and the smallest for thefts. Once the full set

of controls is included, the father – son relationships can be fully accounted for in all

cases except violent crimes and thefts.

As is the case for the extensive margin, the strongest intergenerational

relationships at the intensive margin are also seen for thefts and traffic offenses.

However, controlling for socioeconomic status and ability explain much less of the father

– son relationship than it did at the extensive margin (ranges from 13 percent for fraud to

34 percent for theft). At the intensive margin, adding in social support leaves all father-

son crime relationships insignificant. In fact, for theft, it even drives the point estimate

from 0.36 to 0.06. One potential interpretation of this is that social support may be

capturing individuals committing crimes out of need.24

Digging Deeper into the Social Support Mechanism

The results presented thus far consistently indicate that receiving social support

accounts for much of the intergenerational criminal relationships. But, households may

24 Once again, the results for daughters are quite similar to those for sons.

24

receive social support for any number of reasons;25 thus, we have yet to identify why

social support is so closely related to father – child crime relationships. Table 12

examines the relationship between father criminality and receiving any household support

from 1953 to 1972.26 Column (1) indicates that households with criminal fathers are 37

percentage points more likely to receive support than cohort members with non-criminal

fathers. One possibility is that children with criminal fathers receive social support

because the father is incarcerated and not supporting the household. Column (2) of Table

12 thus controls for whether the father was in prison at least once. Households with

incarcerated fathers are significantly more likely to receive social support, and this

accounts for a portion of the relationship between father criminality and support. But,

much is left unexplained.

25 As mentioned earlier, the Swedish system of means-tested social support is not targeted at specific groups such as single mothers. All residents who are incapable of supporting themselves are potentially eligible for support. The system is regulated by national legislation, but is administered in the municipalities by social workers who are supervised by local politicians and by the courts. In some cases, social support is paid out in kind. Clients may receive, e.g., a telephone, television, refrigerator, furniture, or public transportation card instead of a cash payment. In our data set, these in kind payments are simply recorded as cash payments. In most cases, social support is paid out in cash on a monthly basis. In some cases, it can be paid out weekly or even daily. During the time period that we are interested in, 1953 – 1972, cash payments were not routinized. Clients had to meet with their social worker to receive each payment. During most of the 20th Century, 5 to 8 percent of the population received means-tested social support for one or more months each year (Stenberg, 2000). Since WW II, old-age pensioners have been replaced by young families with children as the single largest category of welfare recipients (Stenberg, 2000). In 1950, 9 percent of all single-mothers received social support. This number rose to 17 percent during the 1980s (Stenberg, 2000). In our data set, 21 percent of all cohort members lived in families that received social support at least once during the period 1953 to 1972. The average number of years during which they received support (for one or more months) is 4.88 (4.45). The median number of years during which they received support (for one or more months) is 3. The 5 strongest predictors of receiving social support in our data are (in order of their importance); psychological problems of parents, alcoholism of parents, single household (predominantly single mothers), “incidents” of drunkenness by the mother, and father’s criminality (see Table 15). The median number of years during which our single households received support (for one or more months) is 1. For parents with psycholigiacl problems or serious alcohol problems the median is 4 and 5 years, respectively. These simple facts reconfirm Stenberg’s (2000) conclusion that families who fall through the safety net provided by the Swedish system of universal social benefits and must rely on means-tested assistance are a more negatively selected group with respect to different types of non-economic, social problems. 26 A parallel analysis was conducted for the number of years of support, yielding qualitatively identical results. Households with criminal fathers receive, on average, 2.6 more years of support than those with non-criminal fathers.

25

Similarly, it is possible that households with criminal fathers receive support

because the household is a single parent household; even if the father is not in prison, a

mother may opt to be separated from a criminal father. Column (3) controls for

household structure in 1960 and column (4) controls for whether the mother was the only

parent in the household in 1964. While these variables are themselves significantly

related to the receipt of support, they explain little of the criminal father relationship.

Another possibility is that households with criminal fathers receive support

because the father’s legal income is sufficiently low to justify support. Unfortunately, we

have only single-year measures of parental income. Columns (5), (6), and (7) control for

social class in 1953, the household head’s employment status in 1960, and the father’s

income in 1963, respectively. Including these three variables yields a decrease in the

relationship between father criminality and social support, from a coefficient of 0.326 to

0.226. Column (8) controls for the number of children in the household in 1960; though

related to support, this has little impact on the father coefficient.

Lastly, columns (9) and (10) of Table 12 consider the possibility that households

with criminal fathers are more likely to receive social support because of parental

instability such as mental illness27 or alcoholism.28 These are strong predictors of

receiving support. Cohort members with parents who have been identified to have mental

health problems are, on average, 61 percentage points more likely to receive support.

27 Data on parental mental illness is taken from the Child Welfare Committees. The definition used here is that either the mother or the father has shown symptoms of mental illness or psychiatric problems during the period 1953 - 1972. We know if they suffer from depression or other psychiatric problems, if they are receiving treatment, or if they have committed suicide. In total, 6.5 percent of our parents have problems that have been recorded by the Child Welfare Committees. 28 Data on parental alcoholism is taken from the Child Welfare Committees. In total, 4.4 percent of our parents have been classified as alcoholics by the Child Welfare Committees. An additional 1.9 percent have records of “incidents of drunkenness”.

26

These variables also account for much of the relationship between father criminality and

social support.

5. Explaining Father-Son Criminal Correlations: Can We Identify a Direct Effect?

Sibling Correlations

The Stockholm birth cohort includes 144 identifiable pairs of twins. This enables

us to study the importance of fathers’ criminality and other family background effects by

calculating sibling correlations in criminal behavior.29 Sibling correlations can be viewed

as omnibus measures of the importance of family and community effects. They include

anything shared by siblings (e.g. parental characteristics, such as fathers’ criminality), as

well as things not directly experienced in the home (e.g. school, church and neighborhood

effects). Genetic traits not shared by siblings, differential treatment of siblings, time-

dependent changes in neighborhoods, schools, etc. are all excluded from this correlation.

The data also allows us to distinguish between monozygotic and dizygotic

twins.30 Comparing sibling correlations in criminal behavior of the two types of twins

gives us a direct measure of the potential importance of inherited traits for criminal

behavior and, hence, a direct (genetic) link between fathers’ criminality and that of their

29 Snell (1993) finds that nearly one-third of all inmates in U.S. states prisons in 1991 report having a brother who has also been incarcerated, pointing towards the potential importance of family background. Mazumder (forthcoming) reports sibling correlations in illegal drug as well as extensive margin correlations for spending time in jail. Johnson (2007) examines both sibling and neighborhood correlations in order to ascertain the importance of family background in determining criminal outcomes. 30 The zygotivity of these twins has been determined by matching cohort members to the Swedish twins register maintained by Karolinska Institutet. They deem that monozygotic twins can be classified with up to 99 percent certainty, while dizygotic twins can be classified up to 95 percent certainty.

27

children. If genetics matter, then the correlation between monozygotic twins criminal

behavior should be larger than that found for dizygotic twins.31

We first examine whether or not these 288 individuals differ in any significant

way from the rest of our sample in terms of their criminal behavior and their fathers’

criminality. We find that 31 percent of the male twins are in the official police registrar

(PBR) as compared with 33 percent in the full sample. The mean number of crimes

committed is also roughly equal between these two groups. Only 8 percent of the fathers

of male twins have been sentenced at least once, while 12 percent of the fathers of male

cohort members in the full sample have been sentenced.

Female twins appear somewhat less frequently in the PBR than females in the full

sample, 5 percent versus 7 percent, respectively. The mean number of crimes committed

is also lower for our sample of female twins. Their fathers, on the other hand, are quite

similar to those in the full sample. There are, however, only 6 female twins in the PBR

and we never find both female twins from the same family in the PBR. This means that

we cannot identify a separate family effect using a sample of female twins only. We can,

however, use these observations when estimating sibling correlations for the whole

sample of twins and for mixed gender twins.32

31 For a review of the existing twins studies that us monozygotic (MZ) and dizygotic (DZ) twins differences to study the effect of genetics on the predisposition to commit crime see Ishikawa and Raine (2002). They also discuss the potential limitations of this identification strategy. Parents of MZ twins may treat them more similarly than the parents of DZ twins treat their children. In this case, parental treatment could bias the MZ sibling correlation upwards. On the other hand, if MZ twins strive to differentiate themselves from each other, then this would bias MZ sibling correlations downwards. There can also be greater differences in the birth weights of MZ twins than DZ twins due to the foeto-foetal transfusion syndrome. MZ twins also run a higher risk of certain perinatal complications. Such non-genetic, biological factors may bias MZ correlations downwards. There is evidence that all of these biases do, in fact, exist to some degree. However, Rutter et al. (1990) suggest that they cancel each other out. 32 We have a seventh female twin that we cannot match to a twin brother or sister (and is, therefore, excluded from the twins sample) that is highly criminal with a total of 29 crimes recorded in the PBR. Her father is also an above average criminal with 4 sentences, including one prison sentence. Including this

28

We continue our investigation by presenting a set of sibling correlations in

criminal behavior at the extensive and intensive margins. If inherited traits account for a

large part of the sibling correlation, then we should be able to order them from highest to

lowest as follows: monozygotic (MZ), unknown zygoticity (MZDZ), all twins (All),

dizygotic twins (DZ). This is exactly what we find for the extensive sibling correlation

calculated using the entire sample of twins (see Table 13). However, the correlation for

monozygotic twins is not statistically significantly larger than the correlation among

dizygotic twins. Furthermore, the sibling correlations calculated using male twins only

actually produce a higher correlation for dizygotic twins than for monozygotic twins,

albeit not significantly higher.

In Table 13, we see that the sibling correlations at the extensive margin are

always significantly larger than their appropriate random benchmarks for the two largest

categories of twins, All and DZ.33 A sibling correlation of 0.48 (for the category all male

twins) implies that almost half of the variation in male criminality at the extensive margin

can be attributed to family background and community effects.34 The other correlations

are comparable in magnitude, but have large standard errors due to extremely small

female in the twins sample raises the mean number of crimes committed by female twins to roughly the same level as the full sample. 33 Our random benchmarks reflect the correlation between randomly matched pairs of individuals taken from the full sample and who have the appropriate characteristics. These are reported along with bootstrapped standard errors. For example, we have 53 pairs of male only twins (15 MZ, 30 DZ and 8 MZDZ). In this case, we draw a random sample of 106 men, match them in pairs, and calculate the criminal correlation. This is done 100 times, which produces an appropriate random benchmark for the mean and standard deviation of this correlation. Our random benchmarks are always near zero. More importantly, the appropriate standard errors widen as we narrow the characteristics of the sample used to calculate each correlation. Note, for example, that the standard errors on the correlations concerning all twin pairs are always smaller than those for the sub-sample of male twin pairs. 34 Estimates of brother correlations in income and in years of schooling for comparable Swedish birth cohorts are approximately 0.25 and 0.45, respectively (Björklund et al., 2007). Using NLSY79 data, Mazumder (forthcoming) estimates sibling correlations in illegal drug use for brothers, sisters and all siblings of 0.300, 0.371 and 0.268, respectively. He also reports extensive margin correlations for spending time in jail of 0.263, 0.000 and 0.152, respectively. Using data from the PSID, Johnson (2007) reports brother and childhood neighborhood correlations for adult incarceration of 0.69 and 0.54, respectively.

29

sample sizes. We do not find any strong evidence in favor of a direct (genetic) link

between the criminality of fathers and their children at the extensive margin, at least not

in our aggregated crime variable.

Similar results hold for the sibling correlations at the intensive margin shown in

Table 13. Once again, our small samples hinder us from investigating the differences

between monozygotic and dizygotic twins. We do see, however, that the overall

importance of family background and community effects is even larger for criminal

intensity. A sibling correlation of 0.63 (for the category all male twins) implies that

nearly two-thirds of the variation in the criminality of men at the intensive margin can be

attributed to family background and community effects.

We have also calculated sibling correlations at the extensive and intensive

margins for our seven crime categories.35 The only two categories that appear to support

the idea that MZ correlations may be higher than DZ correlations are also the two most

frequently committed crimes, stealing and serious traffic offences. We take a closer look

at these correlations in Table 13. For stealing, we find no significant difference between

the correlations for MZ and DZ twins. The average sibling correlation in stealing for men

at both margins is statistically significant and equal to 0.43. Thus, family background

(including fathers’ criminality) and community effects account for 43 percent of the

variation in stealing found in the data.

The case of serious traffic offences results in a different picture. Family

background matters, but we also find that the MZ correlation is always significantly

35 Our sample of twins is too small for us to say anything about violent crimes and narcotic crimes. The sibling correlation for fraud is zero and for “other” we find a zero correlation for both MZ and DZ twins. DZ twins have a stronger sibling correlation in vandalism than MZ twins. But this is due to having too few MZ twins in this category.

30

larger than the DZ correlations. We interpret this result, together with our earlier results

concerning the father – son intensive correlations in drunk and dangerous driving, as

indicators that inherited traits may be of particular importance for explaining serious

traffic offences. We believe that this is mainly due to the prevalence of drunk driving in

this category. In future research, we intend to investigate the importance of family

background (both social and inherited) more carefully for drunk driving.

Adoptee Analysis

The Stockholm birth cohort also includes 138 individuals who were adopted by

either two parents or by the spouse of the birth mother or father.36 We can use this

sample of adopted individuals to help distinguish between the importance of nature and

nurture. If the intergenerational relationship is the same, or stronger, for adopted

individuals, then this points towards the importance of environmental rather than genetic

factors. A weaker relationship indicates that genetics matter.

Specifically, we estimate regression (1), expanding it to include controls for

whether the cohort member was adopted and an interaction between whether he was

adopted and his father’s criminal record. For individuals who have multiple fathers (e.g. a

birth father and an adoptive father) and for whom both fathers are known, then criminal

records of both fathers are included in the data and cannot be distinguished from each

other. However, it is often the case that the biological father is not known or not recorded

36 While the data set does not allow us in the latter case to distinguish between adoptive fathers and mothers, we believe that this is predominantly adoptive fathers given the time period (1950s and 1960s).

31

for adopted individuals, particularly those who are adopted at a young age. Thus, we

believe that the father crime data is primarily representative of the adoptive father.37

The results of this specification for both males and females at the extensive

margin are presented in Table 14. Adopted cohort members are more likely to have a

criminal record themselves, though this result is only significant for females. However,

the coefficient on the interaction term is insignificant. Similarly, conducting the analysis

at the intensive margin yields insignificant interaction coefficients for females and males.

We are reluctant, however, to interpret these insignificant results as “true” zeros

(i.e. evidence in favor of nurture and against nature), since we believe that the large

standard errors are mainly due to the small number of adopted individuals used to

identify the interaction term. The negative point estimate for men is, in fact, quite large in

comparison with the coefficient on fathers’ crime. Furthermore, when pooling the sample

of sons and daughters, significant negative interaction coefficients are seen at both the

extensive and intensive margins; at the intensive margin, the interaction term is

significant both with and without controls.

A significant negative coefficient implies that fathers who are not genetically

linked, i.e. adoptive fathers, do not have as much of an impact on the criminality of the

children as do birth fathers. This would point towards the importance of inherited factors.

In sum, we feel that the results of this experiment should either be viewed as

inconclusive, or as providing weak evidence in favor of the importance of inherited traits

37 This view is also supported by the fact that there is a significantly lower proportion of criminal fathers in our sample of adopted children than there are in the rest of the sample. This makes sense if we assume that the fathers’ crime variable refers to the adoptive father, since they would not be allowed to adopt if they had criminal records. Furthermore, one might suspect that if the fathers’ crime variable was mainly that of the biological father, then the fathers’ crime variable would show a higher prevalence of crime than the sample average as in Mednick et al. (1984), since children to criminal fathers are adopted away more often than children of non-criminal fathers.

32

mechanism in explaining the father – son criminal correlation.38 This would be consistent

with the findings of Mednick, Gabrielli, and Hutchings (1984), who find a significant

relationship between the criminal records of adoptees and their biological parents but not

between adoptees and their adoptive parents.

Timing of Fathers’ Crimes

Another way to get at the mechanisms underlying the intergenerational criminal

correlations is to consider the timing of the fathers’ crimes. If these intergenerational

relationships are completely driven by genetics, then the timing of the fathers’ crime, i.e.

before or after the cohort member’s birth, should not matter. Similarly, the timing of the

crime should not matter if common background effects are the driving factor.39 In

contrast, if a father’s criminality causes his child to engage in criminal activities by

setting a bad example (a role model hypothesis), then the timing of the father’s crimes

should matter. Crimes committed before the child is born or when he is just an infant

should matter less than crimes committed when the child is aware of the importance

and/or meaning of such an event.

Table 15 tests these hypotheses by examining whether the father – child criminal

correlations are greater by allowing fathers who committed crimes only after the birth of

their child to have a different relationship with child criminality than those who

committed crimes only before the birth. Results are presented for sons and daughters as

well as for cohort member crimes committed during both youth and adulthood. In each

38 As a robustness check, we redo the analysis using just those individuals who are adopted before the age of 6, as the father’s crime record is almost surely that for the adoptive father in this sample. Once again, insignificant coefficients are found for the interaction term. 39 Actually, crimes due to schizophrenia or any other potentially inherited illness that manifests itself at specific ages would nullify this identification strategy.

33

of these categories, the point estimate associated with crimes committed by fathers after

1953 is greater than that for crimes committed before 1953, though this difference is not

always significant. This pattern of results is generally stronger for sons than daughters.

For instance, sons with fathers who only committed crimes prior to 1953 are 6.5

percentage points more likely to commit a juvenile crime while sons with fathers who

only committed crimes after 1953 are 14.7 percentage points more likely to have a

juvenile record. These two coefficients significantly differ from each other at the one

percent level. Thus, the results provide evidence that the timing of the father’s crime does

matter. The intergenerational relationships are not completely driven by genetics or

common factors; rather, some of this relationship appears to be attributable to

environmental factors, such as the father serving as a bad role model.

One potential alternative explanation of this pattern of results is that fathers, on

average, commit less serious crimes before their children are born because the fathers are

relatively young. As the fathers age, one may expect that only the relatively more serious

criminals will still be committing crime when their children are older. Thus, it seems

reasonable to ask whether we observe stronger father-son crime relationships when

considering crimes committed after 1953 because these children are simply being

influenced by more serious criminals. Our first response to such a critique is that we

focus on fathers who only commit crimes before or after 1953. Thus, those committing

crimes after 1953 are not necessarily worse criminals; they have not built up a criminal

history prior to 1953. In addition, this pattern of results is generally robust to: (i) adding

in a control for the father’s year of birth, (ii) considering separate samples of older and

34

younger fathers, defined according to the median father, and (iii) using pre or post 1953

incarceration spells rather than any record to proxy for crime severity.

6. External Validity (Generalizability)

Although we study a specific cohort of individuals born in 1953 and living in

Stockholm in 1963, we have a number of strong arguments that support the

generalizability of our results. First, Sweden is not a country free from crime. Although

the number of intentional homicides and drug-related crimes are low in comparison with

the European average and the U.S., auto-theft is significantly more common in Sweden

than in the US and Europe (see Table 1). The EU ICS (2005) has classified Sweden as a

“medium-high crime country” in the EU context. Second, our cohort of Swedish men has

exactly the same cumulative offending rates as a comparable sample of London men and

nearly the same rate as men in California, Philadelphia and Denmark (see Section 2).

Third, we show (below) that Sweden is not an outlier in terms of intergenerational

criminal correlations, at lest not when compared with the United States. Fourth, if one

believes that genetics matters for criminal behavior, then these types of arguments should

not be Sweden specific. Fifthth, the criminology literature has documented similar trends

and patterns in the development and structure of crime in Sweden, Western Europe and

North America (Westfelt 2001, Sarnecki 2003). The criminology literature finds more

similarities than differences in the causes and explanations for crime across Western

countries. That is, there is no Sweden-specific theory of crime (Sarnecki 2003). Together,

all of these different arguments help us to feel confident that our results can be

generalized to other Western European and North American countries.

35

Estimating Intergenerational Criminal Correlations Using Available U.S. Data

There are few U.S. data sets that contain information regarding the criminality of

multiple generations, and those that do contain much less information than is available in

the Stockholm Birth Cohort Study. Thus, while one may be able to estimate some

intergenerational correlations with U.S. data, such analyses will be severely limited in a

number of ways. (i) Crime data for the survey members as well as that for their parents

tends to be self-reported (parental crime information is usually reported by the

respondent). (ii) There is very little information on parental criminality, typically just one

or two questions. (iii) The lack of parental crime information limits analyses to the

extensive margin. (iv) The lack of parental crime information also limits ones ability to

delve into the underlying mechanisms; e.g. by looking at the timing of the parent’s

crimes. (v) Surveys that collect information regarding the criminality of the respondents

are fairly recent and, to date, only have information regarding these behaviors when the

individuals are juveniles or young adults.

Two well known U.S. data sets that ask at least one question related to parental

criminality are the 1997 National Longitudinal Survey of Youth (NLSY97) and the

National Longitudinal Survey of Adolescent Health (Add Health). We estimate

intergenerational criminal correlations in these data sets in order to help assess the

generalizability of our Swedish findings. Descriptive statistics and variable definitions for

these two samples are shown in Table 16.

The NLSY97 sample includes all youths in randomly selected households who

were between the ages of 12 and 16 as of December 31, 1996, including an over sample

of Blacks and Hispanics. During the 2002 survey round, the respondent is asked whether

36

an adult household member was in jail or prison in the past five years and whether the

household member was a parent, a partner or spouse, another relative, or a non-relative.

6.3 percent of 2002 survey respondents reported that an adult household member was

incarcerated in the last five years. However, just 1.4 percent of the respondents indicate

that it was a parent.

We estimate intergenerational correlations using three measures of child

criminality. First, we consider whether the individual reports being arrested in the year

prior to the 2002 survey round. This variable has the same sample size as the parental

incarceration variable; however, it also only captures a snapshot of the respondent’s

criminality. Thus, we also consider whether the respondent was ever arrested and ever

incarcerated prior to the 2004 survey round. These latter two variables have a reduced

sample size due to respondents not being interviewed during at least one relevant survey

round.

Table 17 presents the intergenerational correlations and corresponding sample

sizes for the whole sample and the sample of males. All correlations are significantly

different than zero at the one or five percent levels. The correlation coefficients for the

whole sample are equal to 0.0417, 0.0364, and 0.0699 when considering the relationship

between parental incarceration and respondent (i) arrests in the year prior to the 2002

survey round, (ii) arrests prior to 2004, and (iii) incarceration prior to 2004, respectively.

The corresponding correlation coefficients for the sample of males are 0.0515, 0.0463,

and 0.0872.

When evaluating the size of these correlation coefficients, it is again important to

recognize that the maximum magnitude of these correlations is not equal to one. Rather,

37

the maximum feasible intergenerational correlation can be determined by assigning all

criminal parents to a criminal child; the last two rows of Table 17 present each possible

maximum correlation. For instance, when considering the correlation between parental

incarceration and whether the child was ever arrested, the maximum possible correlation

is 0.1342 for sons. Thus, in this case, the estimated correlation coefficient is about a third

of the size of the maximum.

The Add Health data set is a school-based, longitudinal study of the health-related

behaviors of adolescents and their outcomes in young adulthood. The first wave of data

was collected in 1994 and 1995 while the third wave was collected in 2001 and 2002. We

estimate intergenerational correlations for the wave III public use sample, which contains

4,882 respondents, as it is only during this last wave that respondents were asked whether

their biological father ever served time in prison or jail. This is the only question

concerning parental criminality; approximately 14 percent of the sample reports that their

biological father had been incarcerated. During the same survey round, respondents are

also asked whether they have ever been arrested or taken into custody by the police;

about 11 percent of the sample answers in the affirmative.

Table 18 presents the estimated correlations between these two variables for

whole sample and the sample of males as well as the maximum feasible correlation. The

estimated correlation for the whole sample is 0.0800 and that for the male sample is

0.1125; once again, both correlations are significant at the one percent level. Compared to

both the NLSY97 and the SBC datasets, the maximum feasible correlations are somewhat

higher and equal to about 0.85.

38

The important message to take away from this additional analysis using available

U.S. data is that for males our main Swedish correlation (as a share of the maximum

possible) lies between the two U.S. correlations; i.e., 0.11/0.84 = 0.13 (ADDHEALTH) <

0.12/0.52 = 0.23 (SBC) < 0.05/0.13 = 0.38 (NLSY97). This implies that Sweden does not

appear to be an outlier in terms of intergenerational criminal correlations, at least not

when compared to the U.S.

7. Summary of Results and Policy Implications

We conclude with a summary of our main findings and a discussion of what these

findings imply for policy makers. We focus mainly on results concerning the father – son

criminal association and mention only briefly the important gender differences that we

have found.

We find that sons of fathers with criminal records are 55 percent more likely to

have criminal records than those whose fathers do not have criminal records. Sons of

fathers with multiple criminal records also tend to commit more crimes. For each

additional sentence of the father, the son commits (on average) 1.42 additional crimes.

Taken together, our regression analyses indicate that common background variables, such

as socio-economic status, account for roughly one-fourth of the intergenerational criminal

relationship at the extensive margin. Inherited traits, such as ability, account for

somewhat less of the association. The remainder of the father – son relationship is, in

general, due to other differences across families, and this is primarily captured by

whether or not the household receives social support. Our analysis indicates that having

unstable parents, who suffer from mental illness and/or alcoholism, accounts for the

39

largest share of the relationship between fathers’ criminality and social support. We

conclude, therefore, that being poor is not enough to make young men commit crimes. It

appears that it is the unfortunate combination of fathers’ criminality and parental

instability that are most important. This, in turn, implies a behavioral model of

intergenerational criminal correlations.

However, these regression results do not present direct evidence of a causal link

between fathers’ criminality and that of their children. We, therefore, ran a series of

alternative experiments to collect more direct evidence concerning the father’s role in

producing criminal outcomes in his children. We have focused our attentions upon two,

particular direct channels: (1) inherited traits, and (2) the father as a role model.

In our first experiment, we calculate a number of sibling crime correlations using

the 144 pairs of twins found in our data set. These correlations reaffirmed the importance

of family background for explaining crime. We also found direct evidence that genetics

matters for drunk and dangerous driving, since the sibling correlation for this particular

category of crime was significantly higher for monozygotic twins than for dizygotic

twins. Our sample of adopted children, on the other hand, provided only weak evidence

of the importance of genetic factors. We also found that the timing of the fathers’ crime

mattered for the criminal outcomes of children, which supports the father as a role model

hypothesis.

Though the father – daughter correlation is generally weaker than that for sons

(about half), we find no significant differences in how this pattern is explained. Both of

the direct mechanisms, i.e. inherited traits and the father as a role model, apply to

40

daughters as well as sons. However, the weaker father – daughter association implies that

these direct mechanisms affect daughters less than sons.

Therefore, our research points to three factors that produce the observed

intergenerational criminal correlations: (i) common socio-economic background, (ii)

inherited traits, and (iii) the father’s importance as a role model for his children. The first

implies that policies aimed at reducing poverty also reduce crime. The second factor

leads us into a fairly new literature in psychology and behavioral genetics that tries to

identify inherited traits and/or conditions that can be used as indicators of which children

are more “at risk” and may have a greater need for treatment (see e.g. Moffitt 2005).

Lastly, if the observed intergenerational criminal correlations are (as we believe)

mainly produced by a behavioral model, such as our role model hypothesis, then a new

avenue for fighting crime appears feasible. A policy that successfully treats or deters

criminal behavior today has a larger impact on total crime than previously recognized,

since it also produces a “second generation effect”. Our research also shows that children

growing up in families that have problems with mental illness and/or substance abuse are

particularly susceptible to this behavioral mechanism and to the criminal status of their

parents. Future research on the potential interaction effects between nature and nurture

will be important to the development of this line of reasoning.40 If strong interaction

effects exist, then our “second generation effect” will be even larger.

Another set of important research questions concerns the children of those who

are (or have been) incarcerated. While crime rates have stabilized or even fallen in most

40 Terrie Moffitt (2005) provides an excellent summary of the current state of the science concerning gene-environment interplay in producing antisocial behavior (such as crime).

41

Western countries, incarceration rates continue to rise.41 Little is known about how the

children of these criminal and incarcerated parents will fair. Will they become criminals

themselves? How does an intervention, such as incarceration, impact the

intergenerational transmission of criminality? More generally, can we design policies that

mitigate the negative role (and support the positive role) of the family in reproducing

crime across generations? After all, crime does run in the family.

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41 According to the U.S. Bureau of Justice Statistics (BJS), the size of the incarcerated population increased by more than 300 percent, from about 500,000 to 2.2 million, between 1980 and 2005. In addition, an estimated 5.6 million adults in the U.S. had ever served time in a Federal or State prison as of December 2001; note that this statistic excludes those ever held in a jail. A 2000 BJS report estimates that 721,500 State and Federal prisoners were parents to 1,498,800 children under age 18 in 1999. In 1991, the number of minors with a parent in State or Federal prison was just 936,500. Though the United States has the highest and fastest growing incarceration rate worldwide, these trends are not unique to the United States. Incarceration rates have increased throughout the world during the 1990s. Prison populations grew by over 20 percent in almost all European countries, and by at least 40 percent in half of these countries. Likewise, Australia’s prison population grew by about 50 percent and South Africa’s by 33 percent (Walmsley, 2003). In Sweden, the number of prison admissions increased by more than 20 percent just from 2001 to 2004. As in the United States, increasing crime rates do not tend to accompany these rising prison populations; rather, in many countries, crime rates have stabilized or declined.

42

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Levitt, Steven (1996). "The Effect of Prison Population Size on Crime Rates: Evidence from Prison Overcrowding Litigation." The Quarterly Journal of Economics, 111(2), pp. 319-51. Levitt, Steven (1997). "Using Electoral Cycles in Police Hiring to Estimate the Effect of Police on Crime." The American Economic Review, 87(3), pp. 270-90. Levitt, Steven and John. J. Donohue, III (2001). "The Impact of Legalized Abortion on Crime." Quarterly Journal of Economics, 116(2), pp. 379-420. Mazumder, Bhashkar (forthcoming), “Sibling Similarities and Economic Inequality in the US,” Journal of Population Economics. Mednick, Sarnoff, William Gabrielli and Barry Hutchings (1984), “Genetic Influences in Criminal Convictions: Evidence from an Adoption Cohort,” Science 224(4651), 891-94. Moffitt, Terrie E., Avshalom Caspi, Michael Rutter, Phil A. Silva (2001), Sex Differences in Antisocial Behaviour: Conduct, Disorder, Delinquency, and Violence in the Dunedin Longitudinal Study, Cambridge: Cambridge University Press. Moffitt, Terrie E. (2005), “The New Look of Behavioral Genetics in Developmental Psychopathology: Gene-Environment Interplay in Antisocial Behaviors,” Psychological Bulletin 131(4), 533-54. Murray, Joseph, Carl-Gunnar Janson and David P. Farrington (2007), “Crime in Adult Offspring of Prisoners: A Cross-National Comparison of Two Longitudinal Samples,” Criminal Justice and Behavior 34(1): 133-49. Reyes, Jessica Wolpaw (2007), “Environmental Policy as Social Policy? The Impact of Childhood Lead Exposure on Crime,” NBER Working Paper No. 13097. Rowe, David C. and David P. Farrington (1997), “The Familial Transmission of Criminal Convictions,” Criminology 35(1), 177-201. Rutter, Michael, Patrick Bolton, Richard Harrington, Ann Le Couteur, Hope MacDonald and Emily Simonoff (1990), “Genetic Factors in Child Psychiatric Disorders: A Review of Research Strategies,” Journal of Child Psychology & Psychiatry & Allied Disciplines. 31(1): 3-37. Sarnecki, Jerzy (2003), Introduktion till Kriminologi (Introduction to Criminology), Studentliteratur. Snell, Tracy (1993), “Correctional Populations in the United States, 1991,” Washington, D.C.: U.S. Department of Justice.

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Solon, Gary (1999), “Intergenerational Mobility in the Labor Market,” in Orley C. Ashenfelter and David Card (eds.), Handbook of Labor Economics, vol. 3A, Amsterdam: North-Holland, 1761-1800. Stenberg, Sten-Åke (2000), “Inheritance of Welfare Recipiency: An Intergenerational Study of Social Assistance Recipiency in Postwar Sweden,” Journal of Marriage and the Family 62(1), 228-39. Stenberg, Sten-Åke, Denny Vågerö, Reidar Österman, Emma Arvidsson, Cecelia Von Otter and Carl-Gunnar Janson (2007), ”Stockholm Birth Cohort Study 1953-2003: A new tool for life-course studies,” Scandinavian Journal of Public Health 35(1), 104-10. Tillman, Robert (1987), “The Size of the ‘Criminal Population’: The Prevalence and Incidence of Adult Arrest,” Criminology 25, 57-84. Walmsley, Roy (2003), “Global Incarceration and Prison Trends,” Forum on Crime and Society 3(1-2), 65-78. Westfelt, Lisa (2001), “Brott och Straff i Sverige och Europa: En Studie I Komparativ Kriminologi” (Crime and Punishment in Sweden and Europe: A Study in Comparative Criminology), Department of Criminology, Stockholm University. Williams, Jenny and Robin Sickles (2002), “An Analysis of the Crime as Work Model: Evidence from the 1958 Philadelphia Birth Cohort Study,” The Journal of Human Resources. 37(3), 479-509. Wilson, Harriett (1987), “Parental Supervision Re-examined,” British Journal of Criminology 27(3), 275-301. Wolfgang, Marvin E., Robert M. Figlio and Thorsten Sellin (1972), Delinquency in a Birth-Cohort, Chicago: Chicago University Press.

45

Table 1. Crime Rates per 100,000 People in Sweden, Europe and the U.S in 2000 intentional homicide theft of motor vehicle Offences,

health statistics

Offences, police

statistics

Persons convicted

Offences Suspected offenders

Persons convicted

Swedena 1.0 n.a. 1.7 842 46 19 Europea 3.2 3.0 3.0 275 24 12 U.S.b n.a. 5.5 5.2 414 n.a. n.a. a) Source: European Sourcebook of Crime and Criminal Justice Statistics – 2006. Europe refers to the average of

37 different European countries including Sweden. b) Source: FBI, Uniform Crime Reports.

46

Table 2. Descriptive Statistics of Crime Variables 80

Males (n = 7719)

Females (n = 7398)

Variable Definition Mean SD Mean SD Cohort Member Juvenile Delinquency Variables Crime6072 1 if has record of any offense from 1960 – 72 0.20 0.40 0.06 0.23 Steal6072 1 if was caught stealing, petty theft, motor vehicle

theft, burglary, and other theft from 1960 – 72 0.16 0.36 0.04 0.20

Violent6072 1 if caught committing violent crime from 1960-72 0.07 0.25 0.01 0.08 Narcotic6072 1 if commit alcohol or narcotics abuse from 1960-72 0.04 0.19 0.02 0.15 Other6072 1 if other offense, driving and sex, from 1960-72 0.04 0.21 0.00 0.04 Cohort Member Adult Crime Variables – Extensive Margin Crime 1 if any crime in PBR through 1984 0.33 0.47 0.07 0.26 Violent 1 if violent crime in PBR through 1984 0.08 0.27 0.01 0.10 Steal 1 if theft in PBR through 1984 0.19 0.39 0.04 0.21 Fraud 1 if fraud offense in PBR through 1984 0.06 0.24 0.02 0.13 Traffic 1 if traffic offense in PBR through 1984 0.15 0.36 0.01 0.11 Narcotic 1 if narcotic offense in PBR through 1984 0.04 0.20 0.01 0.10 Vandalism 1 if vandalism offense in PBR through 1984 0.06 0.24 0.00 0.06 Other 1 if other offense in PBR through 1984 0.13 0.34 0.01 0.10 Cohort Member Adult Crime Variables – Intensive Margin CrimeSum Number of crimes in PBR through 1984 3.43 15.35 0.40 4.01 ViolentSum Number of violent crimes in PBR through 1984 0.21 1.21 0.02 0.25 StealSum Number of thefts in PBR through 1984 1.60 9.97 0.14 1.16 FraudSum Number of fraud offenses in PBR through 1984 0.27 2.10 0.10 1.14 TrafficSum Number off traffic offenses in PBR through 1984 0.10 0.59 0.01 0.11 NarcoticSum Number of narcotic offenses in PBR through 1984 0.65 4.16 0.07 1.69 VandalismSum Number of vandalism offenses in PBR through 1984 0.20 1.56 0.04 0.59 OtherSum Number of other offenses in PBR through 1984 0.39 1.75 0.03 0.69 Father Crime Variables – Extensive Margin Father 1 if father has at least one sentence 0.12 0.32 0.13 0.33 ProbationFather 1 if father has at least one probation sentence 0.08 0.28 0.09 0.28 PrisonFather 1 if father has at least one prison sentence 0.03 0.18 0.04 0.19 DrivingFather 1 if father has at least one drunk driving sentence 0.04 0.18 0.04 0.21 ExemptFather 1 if father has at least one exempt sentence 0.01 0.07 0.00 0.07 Father Crime Variables – Intensive Margin FatherSum Total number of father’s sentences 0.25 1.09 0.28 1.10 ProbationFatherSum Number of times father sentenced to probation 0.12 0.46 0.13 0.50 PrisonFatherSum Number of times father sentenced to prison 0.08 0.63 0.08 0.61 DrivingFatherSum Number of times father received drunk driving

sentence 0.05 0.31 0.06 0.35

ExemptFatherSum Number of times father received exempt sentence 0.01 0.14 0.01 0.12

47

Table 3. Crime Statistics for Cohort Members by Fathers Criminality

Father has a

record Father does not have a

record % of sons with an adult record 48.4% 31.2% 918 6801

% of daughters with an adult record 14.3% 5.9% 949 6449 Average # of crimes committed by sons 6.75

918 2.98 6801

Average # of crimes committed by daughters

1.05 949

0.31 6449

Sample sizes in italics.

48

Table 4. Father-Son Criminal Correlations at the Extensive Margin

Crime Violent Steal Fraud Vandalism Traffic Narcotic Other

Father 0.118*** (0.0116)

0.103*** (0.0141)

0.123*** (0.0131)

0.076*** (0.0150)

0.075*** (0.0150)

0.084*** (0.0123)

0.057*** (0.0152)

0.090*** (0.0144)

ProbationFather 0.113*** (0.0129)

0.0946*** (0.0151)

0.117*** (0.0137)

0.081*** (0.0152)

0.066** (0.0139)

0.083*** (0.0126)

0.059** (0.0160)

0.082*** (0.0135)

PrisonFather 0.071*** (0.0122)

0.048*** (0.0149)

0.073*** (0.0140)

0.048*** (0.0144)

0.042** (0.0149)

0.063*** (0.0132)

0.027** (0.0136)

0.057*** (0.0156)

DrivingFather 0.064*** (0.0107)

0.079*** (0.0150)

0.068*** (0.0125)

0.033*** (0.0127)

0.069*** (0.0150)

0.043*** (0.0142)

0.042*** (0.0143)

0.061*** (0.0140)

ExemptFather 0.028*** (0.0107)

0.045** (0.0203)

0.014 (0.0122)

0.056*** (0.0211)

0.020 (0.0172)

0.013 (0.0132)

0.019 (0.0167)

-.002 (0.0118)

Numbers in parentheses are bootstrapped standard errors. Significance levels; 1% ***, 5% **, 10% *.

49

Table 5. Father-Daughter Criminal Correlations at the Extensive Margin

Crime Violent Steal Fraud Vandalism Traffic Narcotic Other

Father 0.110*** (0.0143)

<<0.055***

(0.0156) 0.108*** (0.0157)

0.084*** (0.0138)

<<<0.013

(0.0122) 0.054*** (0.0152)

0.071*** (0.0156)

<0.051***

(0.0166)

ProbationFather 0.093*** (0.0157)

<<<0.025*

(0.0143) 0.097*** (0.0176)

0.082*** (0.0146)

<<<0.010

(0.0134)

<<<0.028**

(0.0143) 0.044*** (0.0157)

<<<0.025*

(0.0155)

PrisonFather 0.063*** (0.0150)

0.043** (0.0184)

0.057*** (0.0151)

0.044** (0.0191)

0.010

(0.0153) <0.028*

(0.0159) 0.044** (0.0207)

0.022 (0.0170)

DrivingFather 0.084*** (0.0146)

0.054*** (0.0205)

0.065*** (0.0153)

0.059*** (0.0167)

<<<0.006 (0.0133)

0.072*** (0.0211)

>0.087***

(0.0209) 0.055*** (0.0207)

ExemptFather 0.028

(0.0172) 0.012

(0.0213) 0.024

(0.0188) 0.021

(0.0207) 0.027

(0.0331) 0.044*

(0.0258) 0.052*

(0.0310) 0.013

(0.0211) Numbers in parentheses are bootstrapped standard errors. Significance levels; 1% ***, 5% **, 10% *. Significantly lower than the father-son correlation; 1% <<<, 5% <<, 10% <. Significantly greater than the father-son correlation; 1% >>>, 5% >>, 10% >.

50

Table 6. Father-Son Criminal Correlations at the Intensive Margin

Crime Violent Steal Fraud Vandalism Traffic Narcotic Other

Father 0.101*** (0.0202)

0.077*** (0.0162)

0.060*** (0.0157)

0.083** (0.0424)

0.061*** (0.0153)

0.096*** (0.0306)

0.064*** (0.0247)

0.083*** (0.0189)

ProbationFather 0.070*** (0.0152)

0.076*** (0.0213)

0.032*** (0.0095)

0.055*** (0.0192)

0.045*** (0.0131)

0.075*** (0.0225)

0.043*** (0.0173)

0.083*** (0.0225)

PrisonFather 0.066*** (0.0219)

0.043*** (0.0172)

0.038** (0.0184)

0.067 (0.0422)

0.028** (0.0117)

0.066** (0.0317)

0.051 (0.0337)

0.036*** (0.0143)

DrivingFather 0.085*** (0.0247)

0.047*** (0.0150)

0.074*** (0.0271)

0.014 (0.0107)

0.087*** (0.0229)

0.052** (0.0248)

0.049** (0.0222)

0.081*** (0.0259)

ExemptFather 0.071** (0.0329)

0.054* (0.0287)

0.025 (0.0174)

0.131* (0.0706)

0.010 (0.0113)

0.094** (0.0483)

0.017 (0.0118)

0.038 (0.0309)

Numbers in parentheses are bootstrapped standard errors. Significance levels; 1% ***, 5% **, 10% *.

51

Table 7. Father-Daughter Criminal Correlations at the Intensive Margin

Crime Violent Steal Fraud Vandalism Traffic Narcotic Other

Father 0.067*** (0.0215)

0.042* (0.0239)

0.099*** (0.0250)

0.049*** (0.0180)

<<<0.008

(0.0095)

<0.029

(0.0217) 0.046** (0.0202)

<<<0.017

(0.0187)

ProbationFather 0.051** (0.0217)

0.043 (0.0311)

>>0.101***

(0.0298) 0.045*** (0.0172)

<0.011

(0.0146)

<<<-.001

(0.0064) 0.032

(0.0205)

<<<0.010

(0.0177)

PrisonFather 0.039** (0.0178)

0.012 (0.0115)

0.048*** (0.0183)

0.027* (0.0148)

<<<-0.003

(0.0029) 0.029

(0.0237) 0.023

(0.0187) 0.007

(0.0131)

DrivingFather 0.059*** (0.0196)

0.032* (0.0169)

0.064*** (0.0165)

0.043** (0.0196)

<<<0.008

(0.0141) 0.037

(0.0270) 0.046** (0.0206)

<<0.019

(0.0166)

ExemptFather 0.034

(0.0374) 0.050

(0.0556) 0.057

(0.0580)

<0.003

(0.0059) 0.018

(0.0240)

<0.009

(0.0117) 0.035

(0.0367) 0.021

(0.0339) Numbers in parentheses are bootstrapped standard errors. Significance levels; 1% ***, 5% **, 10% *. Significantly lower than the father-son correlation; 1% <<<, 5% <<, 10% <. Significantly greater than the father-son correlation; 1% >>>, 5% >>, 10% >.

52

Table 8. Correlations between Fathers’ Criminality and Juveniles’ Delinquent Behavior at the Extensive Margin

Crime Steal Violent Narcotic/ Alcohol

Other Crime Steal Violent Narcotic/ Alcohol

Other

Sons Aged 7 to 12 Daughters Aged 7 to 12

0.076***

(0.0143) 0.066***

(0.0138) 0.057***

(0.0168) -.008***

(0.0023) -.009***

(0.0020) 0.051***

(0.0170) 0.046***

(0.0167) 0.046***

(0.0180)

>>>0.030***

(0.0104) no

obs.

Sons Aged 13 to 19 Daughters Aged 13 to 19

0.112***

(0.0131)+ 0.110***

(0.0131)++ 0.097***

(0.0154)+ 0.044***

(0.013)+++ 0.048***

(0.0116)+++ 0.124***

(0.0153)+++ 0.116***

(0.0153)+++

<<<0.020 (0.0157)

>>>0.082***

(0.0173)+++ 0.019

(0.0167)

Numbers in parentheses are bootstrapped standard errors. Significance levels; 1% ***, 5% **, 10% *. Significantly lower than the father-son correlation; 1% <<<, 5% <<, 10% <. Significantly greater than the father-son correlation; 1% >>>, 5% >>, 10% >. Significantly greater than correlation when young; 1% +++, 5% ++, 10% +.

53

Table 9. Explaining the Relationship Between Father and Son Criminality at the Extensive Margin (1) (2) (3) (4) (5) (6)

father 0.172** 0.127** 0.100** 0.058** 0.049** 0.062** (0.017) (0.018) (0.018) (0.018) (0.019) (0.020)

missing53type1 0.012 0.014 -0.027 -0.026 0.098 (0.208) (0.202) (0.192) (0.194) (0.231)

lowerwhitecollar53 0.063* 0.045 0.042 0.047 0.043 (0.024) (0.024) (0.024) (0.025) (0.027)

lowerentrepreneur53 0.055 0.023 0.018 0.018 0.012 (0.033) (0.033) (0.033) (0.033) (0.036)

skilledbluecollar53 0.098** 0.052 0.044 0.048 0.050 (0.028) (0.028) (0.028) (0.028) (0.030)

unskilledbluecollar53 0.115** 0.056 0.043 0.049 0.046 (0.030) (0.029) (0.029) (0.029) (0.032)

missing53type2 0.150** 0.087* 0.084* 0.093* 0.074 (0.041) (0.041) (0.041) (0.041) (0.045)

missing63type1 0.140** 0.066 0.021 0.002 0.012 (0.042) (0.042) (0.041) (0.040) (0.044)

lowerwhitecollar63 0.019 -0.010 -0.011 -0.013 0.004 (0.022) (0.022) (0.022) (0.022) (0.025)

lowerentrepreneur63 0.065* 0.031 0.033 0.028 0.047 (0.031) (0.031) (0.031) (0.031) (0.034)

skilledbluecollar63 0.068* 0.017 0.009 0.009 0.023 (0.027) (0.027) (0.027) (0.027) (0.029)

unskilledbluecollar63 0.112** 0.041 0.026 0.024 0.047 (0.029) (0.029) (0.028) (0.028) (0.032)

swedish 0.031 0.046 0.046 0.046 0.049 (0.031) (0.031) (0.032) (0.032) (0.034)

employhh60 -0.022 -0.016 0.007 0.011 0.012 (0.019) (0.020) (0.020) (0.020) (0.022)

dadincome63 -0.001* -0.001 -0.000 -0.000 -0.000 (0.000) (0.000) (0.000) (0.000) (0.000)

verbal -0.000 0.000 -0.001 -0.001 (0.001) (0.001) (0.001) (0.001)

spatial -0.000 -0.000 -0.000 0.000 (0.001) (0.001) (0.001) (0.001)

math 0.001 0.001 0.001 0.001

54

(0.001) (0.001) (0.001) (0.001)

Dinstructions -0.013* -0.012* -0.011* -0.013* (0.005) (0.005) (0.005) (0.006)

Dverbal -0.012* -0.012* -0.012** -0.014** (0.005) (0.005) (0.005) (0.005)

Dspatial 0.010* 0.009* 0.010* 0.010* (0.004) (0.004) (0.004) (0.004)

Dtechnical -0.007 -0.006 -0.006 -0.005 (0.004) (0.004) (0.004) (0.004)

grade6marks -0.001** -0.001** -0.001** -0.001** (0.000) (0.000) (0.000) (0.000)

anysocsupp 0.080** 0.064** 0.061** (0.019) (0.019) (0.021)

yrssocsupp 0.010** 0.006* 0.007* (0.003) (0.003) (0.003)

alcpar5372 0.068* 0.086** (0.031) (0.033)

fatherdrunk 0.060 0.043 (0.036) (0.039)

motherdrunk 0.207 0.184 (0.124) (0.129)

psychpar5372 0.032 0.021 (0.024) (0.025)

fardeath5372 0.155** 0.146** (0.046) (0.049)

momdeath5372 0.092 0.069 (0.065) (0.067)

absence 0.043** 0.052** (0.013) (0.014)

smokeyes 0.104** 0.112** (0.014) (0.015)

Neighborhood Fixed Effects

NO NO NO NO NO YES

Observations 7719 7719 7719 7719 7719 7335 Standard errors in parentheses. * significant at 5%; ** significant at 1%. Estimates a probit regression and reports marginal effects. Missing observations are replaced with the variable mean and dummy variables indicating that the observations are missing are included but not presented in the above table. Note that the sample size decreases when neighborhood fixed effects are included because some neighborhoods have so few people included that the neighborhood is a perfect predictor of cohort member criminality; these individuals get dropped from the analysis.

55

Table 10. Explaining the Relationship Between Father and Son Criminality at the Intensive Margin

(1) (2) (3) (4) (5) (6)

fathersum 1.415** 1.208** 1.068** 0.570 0.365 0.333 (0.292) (0.300) (0.304) (0.322) (0.335) (0.347) Includes Controls for: SES NO YES YES YES YES YES Ability NO NO YES YES YES YES Social Support NO NO NO YES YES YES Other Family Characteristics

NO NO NO NO YES YES

Neighborhood Fixed Effects

NO NO NO NO NO YES

Observations 7719 7719 7719 7719 7719 7717 R-squared 0.01 0.03 0.06 0.08 0.10 0.15

56

Table 11. Explaining the Relationship Between Father and Son Criminality – By Crime Type (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14) (15) (16) (17) (18) violent violent violent steal steal steal fraud fraud fraud Vand. Vand. Vand. traffic traffic traffic narcotic narcotic narcotic

Extensive Margin for Sons father 0.085** 0.040** 0.018* 0.149** 0.081** 0.041** 0.056** 0.026** 0.011 0.055** 0.027** 0.008 0.094** 0.048** 0.022 0.036** 0.014* 0.004 (0.012) (0.009) (0.008) (0.016) (0.015) (0.015) (0.011) (0.008) (0.007) (0.011) (0.008) (0.007) (0.015) (0.013) (0.013) (0.009) (0.006) (0.005) Intensive Margin for Sons fathersum 0.085** 0.063** 0.022 0.546** 0.361* -0.076 0.160* 0.140 0.109 0.033** 0.023** 0.004 0.366** 0.302* 0.191 0.091* 0.078* 0.056 (0.021) (0.022) (0.023) (0.135) (0.144) (0.169) (0.072) (0.075) (0.083) (0.009) (0.009) (0.008) (0.129) (0.126) (0.122) (0.038) (0.037) (0.042) No Controls YES NO NO YES NO NO YES NO NO YES NO NO YES NO NO YES NO NO SES and Ability Controls

NO YES NO NO YES NO NO YES NO NO YES NO NO YES NO NO YES NO

All Controls

NO NO YES NO NO YES NO NO YES NO NO YES NO NO YES NO NO YES

Each cell presents the coefficient associated with a father criminality from a separate specification. The dependent variable is either the number of crimes committed by the cohort member in a particular crime category or whether the cohort member committed any crimes in that category; i.e. the intensive and extensive margins respectively. Extensive margin specifications are estimated with a probit model and presents marginal effects while intensive margin specifications are estimated with OLS. Standard errors in parentheses. * significant at 5%; ** significant at 1%. Missing observations are replaced with the variable mean and dummy variables indicating that the observations are missing are included but not presented in the above table.

57

Table 12. Correlates of Household Receiving Any Social Support from 1953-1972 (1) (2) (3) (4) (5) (6) (7) (8) (9) (10)

father 0.370** 0.328** 0.322** 0.326** 0.278** 0.275** 0.226** 0.212** 0.186** 0.148** (0.012) (0.014) (0.014) (0.014) (0.014) (0.014) (0.014) (0.014) (0.014) (0.014)

prisonfather 0.127** 0.105** 0.098** 0.086** 0.085** 0.061** 0.055** 0.059** 0.049* (0.023) (0.023) (0.023) (0.022) (0.022) (0.019) (0.019) (0.020) (0.020) separhh60 0.258** 0.118** 0.123** 0.103** 0.082** 0.105** 0.101** 0.099** (0.021) (0.023) (0.022) (0.022) (0.019) (0.019) (0.020) (0.020) singlehh60 0.248** 0.106** 0.089** 0.083** 0.063** 0.159** 0.153** 0.155** (0.024) (0.025) (0.024) (0.024) (0.021) (0.021) (0.022) (0.022) widowhh60 0.176** 0.043 0.057* 0.020 0.011 0.043* 0.049* 0.057** (0.023) (0.025) (0.024) (0.025) (0.021) (0.021) (0.022) (0.022) divorcehh60 0.276** 0.140** 0.133** 0.118** 0.097** 0.139** 0.124** 0.122** (0.015) (0.017) (0.016) (0.016) (0.014) (0.014) (0.014) (0.015) mor_alone64 0.209** 0.199** 0.197** 0.131** 0.124** 0.121** 0.117** (0.013) (0.012) (0.012) (0.014) (0.014) (0.014) (0.015) uppermiddle53 -0.172** -0.171** -0.076** -0.086** -0.116** -0.116** (0.010) (0.010) (0.017) (0.015) (0.011) (0.011) lowerwhitecollar53 -0.082** -0.082** 0.000 0.003 -0.049** -0.047** (0.019) (0.019) (0.021) (0.021) (0.017) (0.018) lowerentrepreneur53 -0.055** -0.056** -0.001 -0.007 -0.058** -0.056** (0.019) (0.019) (0.024) (0.023) (0.015) (0.016) skilledbluecollar53 0.023 0.022 0.070** 0.068** 0.009 0.014 (0.022) (0.022) (0.025) (0.024) (0.020) (0.020) unskilledbluecollar53 0.074** 0.074** 0.113** 0.100** 0.038 0.040 (0.025) (0.025) (0.028) (0.027) (0.022) (0.022) employhh60 -0.078** -0.059** -0.050** -0.034** -0.031** (0.011) (0.010) (0.010) (0.010) (0.010) dadincome63 -0.008** -0.007** -0.007** -0.006** (0.000) (0.000) (0.000) (0.000) hhnumchild60 0.060** 0.056** 0.055** (0.003) (0.003) (0.003) psychpar5372 0.608** 0.568** (0.019) (0.020) alcpar5372 0.329** (0.028) fatherdrunk 0.114** (0.024) motherdrunk 0.153 (0.098) Observations 15118 15118 15118 15118 15117 15117 15117 15117 15117 15117 Standard errors in parentheses. * significant at 5%; ** significant at 1%. Missing observations are replaced with the variable mean and dummy variables indicating that the observations are missing are included but not presented in the above table.

58

Table 13. Sibling Correlations in Crime at the Extensive and Intensive Margins Extensive Intensive

Twin Pairs MZ MZDZ All DZ MZ MZDZ All DZ

Crime

All 0.475

(0.2043)

0.471**

(0.1666)

0.364***

(0.0954)

0.297*

(0.1412)

0.316

(0.1988)

0.985**

(0.3118)

0.332

(0.2337)

0.044

(0.1527)

Benchmark 0.004

(0.2222)

-.011

(0.2839)

0.005

(0.0864)

-.000

(0.0999)

-.027

(0.1851)

-.007

(0.3068)

-.011

(0.076)

0.013

(0.1093)

Male 0.339

(0.2463)

0.333

(0.1426)

0.476***

(0.1112)

0.627**

(0.1860)

0.208

(0.2389)

0.987 *

(0.4221)

0.632**

(0.2689)

0.183

(0.3626)

Benchmark -.028

(0.2635)

-.040

(0.3917)

-.004

(0.1243)

0.010

(0.1823)

-.003

(0.2688)

-.016

(0.3689)

-.010

(0.1041)

-.009

(0.189)

Steal

All

0.377

(0.2359)

0.679

(0.1859)

0.3192

(0.1118)

0.224

(0.1516)

0.472

(0.3520)

0.999

(0.0004)

0.238

(0.2130)

0.004

(0.1101)

Male

0.342

(0.2705)

0.655

(0.2108)

0.430

(0.1482)

0.447

(0.2492)

0.437

(0.4055)

0.999

(0.0003)

0.432

(0.2764)

0.030

(0.3534)

Traffic

All

0.849>>>

(0.1356)

0.531

(0.1702)

0.456

(0.1108)

0.250

(0.1827)

0.755>>>

(0.1404)

0.963

(0.0179)

0.231

(0.1907)

0.050

(0.1533)

Male

0.829>>

(0.1596)

0.488

(0.2135)

0.495

(0.1569)

0.294

(0.2250)

0.710>

(0.1464)

0.965

(.01478)

0.565

(0.1776)

0.192

(0.2727)

Numbers in parentheses are bootstrapped standard errors. Significantly larger than random benchmark; 1% ***, 5% **, 10% *. MZ correlation larger than DZ correlation; >>> 1%, >> 5%, > 10%.

59

Table 14. Adoption and Intergenerational Criminal Correlations at the Extensive Margin (1) (2) (3) (4) (5) (6) (7) (8)

Father 0.172** 0.172** 0.174** 0.050** 0.084** 0.083** 0.085** 0.026** (0.017) (0.017) (0.017) (0.019) (0.012) (0.012) (0.012) (0.010)

Adopted 0.081 0.102 0.085 0.107* 0.131* 0.125* (0.058) (0.061) (0.062) (0.047) (0.058) (0.056)

Father*Adopted -0.195 -0.153 -0.036 -0.031 (0.121) (0.133) (0.030) (0.027)

Sample Male Male Male Male Female Female Female Female Controls NO NO NO YES NO NO NO YES Observations 7719 7719 7719 7719 7399 7399 7399 7398 Standard errors in parentheses. * significant at 5%; ** significant at 1%. Estimates a probit regression and reports marginal effects. Missing observations are replaced with the variable mean and dummy variables indicating that the observations are missing are included but not presented in the above table.

60

Table 15. The Timing of Father Criminality (1) (2) (3) (4) (5) (6) (7) (8)

Sons Daughters

Dependent Variable: Any Crime as an

Adult Any Crime as a

Juvenile Any Crime as an

Adult Any Crime as a

Juvenile

father 0.172** 0.139** 0.084** 0.086** (0.017) (0.016) (0.012) (0.011)

fatherpre53only 0.115** 0.065** 0.066** 0.069** (0.028) (0.024) (0.020) (0.019)

fatherpost53only 0.161** 0.147** 0.077** 0.087** (0.024) (0.023) (0.017) (0.016)

Test that pre and post coefficients are equal

p = 0.19 p = 0.01 p = 0.63 p = 0.44

Observations 7719 7719 7719 7719 7399 7399 7399 7399 Standard errors in parentheses. * significant at 5%; ** significant at 1% . All specifications estimated with a probit model and present the marginal effects. No additional controls are included. Father criminality is defined in three ways: (i) whether the father committed any crimes, (ii) whether the father only committed crimes prior to 1953, and (iii) whether the father only committed crimes after 1953. The dependent variable is listed at the top of each column.

61

Table 16. Summary Statistics for NLSY97 and ADDHEALTH Data

Variable Definition # Obs. Mean Std. Dev.

NLSY97 Incar_Parent 1 if the parent was in jail or prison

during the last five years 7897 0.014 0.12

Arrest_Child02 1 if respondent arrested at least once in year prior to 2002 survey round

7897 0.065 0.25

Ever_Arrest_Child04 1 if respondent was ever arrested prior to 2004 survey round

7133 0.35 0.48

Ever_Incar_Child04 1 if respondent was ever incarcerated prior to 2004 survey round

6543 0.079 0.27

ADDHEALTH Incar_Father 1 if biological father ever served time

in jail or prison 4574 0.14 0.35

Ever_Arrest_Child 1 if respondent was ever arrested or taken into custody by police

4656 0.11 0.32

62

Table 17. Intergenerational Criminal Correlations for NLSY97 Data

Sample Arrest_Child02 Ever_Arrest_ Child04

Ever_Incar_ Child04

Incar_Parent Whole 0.0417 (.017)* 0.0364 (.012)** 0.0699 (.019)** 7896 6905 6498 Incar_Parent Male 0.0515 (.021)* 0.0463 (.015)** 0.0872 (.024)** 3997 3530 3233

Maximum Possible Correlation from Simulated Data Incar_Parent Whole 0.4566 (.021)** 0.1788 (.008)** 0.4362 (.021)** 7896 6905 6498 Incar_Parent Male 0.3492 (.022)** 0.1342 (.011)** 0.3186 (.022)** 3997 3530 3233 Bootstrapped standard errors in parentheses. * indicates significant at 5% level. ** indicates significant at 1% level. Sample sizes are in italics.

63

Table 18. Intergenerational Criminal Correlations for ADDHEALTH Data Sample Ever_Arrest_Child Incar_Father Whole 0.0800 (.017)** 4574 Incar_ Father Male 0.1125 (.024)** 2096

Maximum Possible Correlation from Simulated Data Incar_ Father Whole 0.8552 (.010)** 4574 Incar_ Father Male 0.8373 (.015)** 2096 Bootstrapped standard errors in parentheses. * indicates significant at 5% level. ** indicates significant at 1% level. Sample sizes are in italics.