childhood criminal behavior
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earnings.pd_.pdf
THE IMPACT OF YOUTH CRIMINAL BEHAVIOR
ON ADULT EARNINGS
Sam Allgood University of Nebraska
David B. Mustard University of Georgia
Ronald S. Warren, Jr. University of Georgia [email protected]
September 1999
Abstract
Individuals charged with or convicted of a criminal offense when young complete fewer years of schooling and accumulate less work experience as young adults than those with no contact as a youth with the criminal-justice system. Because both schooling and experience are positively correlated with earnings, having a criminal background when young indirectly lowers earnings as an adult. We show, however, that – holding these human-capital variables constant – youth criminal behavior directly reduces subsequent earnings as an adult.
We combine data from the 1980 wave of the National Longitudinal Survey of Youth, which provides detailed, self-reported information on criminal background, with socioeconomic and demographic variables to specify and estimate a model of the determinants of earnings in 1983 and 1989. The results imply that having been convicted prior to 1980 of a crime when young reduces 1983 earnings by at least 12%. However, having been charged - but not convicted - of an offense as a youth has no statistically significant effect on such earnings. A criminal case adjudicated in juvenile court reduces 1983 earnings by at least 9%, while having a charge decided in adult court lowers those earnings by about 14%. The magnitudes of these earnings effects persist over the subsequent six years.
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I. Introduction
It is well known that young people are more likely to engage in illegal activity
than are older individuals. However, the extent to which illegal behavior engaged in as a
youth influences adult socioeconomic outcomes is less clearly understood. For example,
does such activity as a youth persistently affect subsequent labor-market opportunities, or
are its effects relatively short-lived? Our paper analyzes this relationship by estimating
the impact of youth criminal activity on adult labor-market earnings.
Few studies have examined how youth criminal activity affects adult labor-market
outcomes. Instead, the literature has focused on how adult criminal activity affects adult
outcomes. Previous studies have reached conflicting conclusions about the effect of an
adult conviction on subsequent income. Lott (1989, 1992a, 1992b) examined the earnings
of adult federal offenders, and concluded that their post-conviction reduction in income is
statistically significant and is largest for high-income offenders. He argued that the most
important aspect of society’s sanction against criminals is the reduced legitimate earnings
of offenders upon their return to the labor force. Waldfogel (1994b) also studied adult
federal offenders, and found that a first-time conviction reduced employment
probabilities and significantly depressed legitimate income. These effects were largest for
offenders whose pre-conviction jobs required trust.
Conversely, several studies have found that the labor-market effects of a criminal
background are modest in magnitude and duration. Grogger (1995), using a sample of
male arrestees from California, concluded that earnings and employment effects are
relatively short-lived, that convictions have little effect on earnings, and that probation
has no effect on arrestees' subsequent earnings. Waldfogel (1994a) also addressed the
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persistence of labor-market penalties for criminal participation and found that prior to
their current conviction ex-offenders earned less and were less likely to work than first-
time offenders. These earnings and employment gaps grew with the number of prior
convictions.
Nagin and Waldfogel (1998) maintained that criminal participation increases
observed wages shortly after conviction. They argued that conviction reduces access to
career jobs offering stable, long-term employment, and relegates offenders to spot-market
jobs that have higher initial pay, but do not offer stable employment or steadily rising
wages. Consequently, a first conviction has a positive effect on income for those under
age 25 and an increasingly negative earnings impact for offenders over age 30. Nagin and
Waldfogel (1995) studied about 300 London offenders, and concluded that prior
criminality has no effect on job performance, whereas a criminal conviction increases
both job instability and pay. This result is consistent with their other findings that
conviction increases both the income and employment instability of young offenders.
This study is distinguished from the previous literature in two ways. First, our
observations are drawn randomly from the young-adult population. In contrast, other
studies have confined attention to labor-market outcomes for offenders.1 If, however,
offenders are systematically different from non-offenders, previous results may be
affected by this sample-selection bias. Second, the longitudinal nature of our data allows
us to examine the extent to which labor-market penalties for previous criminal activities
persist over workers' early careers. Most studies have examined the effect on income for
only a few (usually no more than three) years after conviction. However, our study
1 Grogger (1992) examined the effect of conviction on employment, and reported results from one
regression that used data from non-offenders.
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follows labor-market performance for at least 10 years after data were collected on prior
contact with the criminal-justice system.
We find that individuals who were convicted of a crime as youths experience a
12% reduction in earnings when they are young adults, holding constant various human-
capital characteristics like education and work experience. However, those who were
charged, but not convicted, of a criminal offense when young suffer no reduction in
early-career earnings, ceteris paribus. Young adults who had one or more criminal cases
adjudicated in juvenile court earned 9% less than their non-offender counterparts, but
adjudication in adult court reduces earnings by an additional 5%. These estimated effects
are found to persist over the subsequent six years. However, individuals who had contact
with the criminal justice system as youths also complete fewer years of schooling and
accumulate less work experience as young adults. Because schooling and experience
increase future earnings, these estimated partial effects of a criminal background
underestimate its total effect on such earnings.
The paper is organized as follows. Section II describes the data. Section III
presents the model, and discusses how we control for person-specific heterogeneity.
Section IV reports the empirical results, and Section V concludes.
II. Data
We use data on males from the 1980, 1984, and 1990 waves of the National
Longitudinal Survey of Youth (NLSY), a stratified random sample of individuals who
were between 14 and 22 years old in 1979. The 1980 wave included a special section
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about the respondents' self-reported participation in delinquent and criminal activities.
This section of the survey provides detailed information about each respondent's history
of criminal charges and convictions, the nature of any offenses committed, and whether
adjudication of a criminal case was in juvenile or adult court. We combine this
information with standard demographic and labor-market data to estimate earnings
equations augmented by a variety of criminal participation variables. The 1984 and 1990
surveys record labor-market earnings for 1983 and 1989, respectively.
Our empirical work uses two distinct samples: one includes individuals through
the 1984 wave of the NLSY, and the second includes individuals through the 1990 wave.
For the first data set we omitted all individuals younger than 21 at the time of the 1984
interview, because many were still in school or just beginning their labor-market
experiences.2 Furthermore, observations were deleted for those reporting zero weeks of
work or zero income and those responding inappropriately.3 Finally, we deleted people
who were students during the week of the interview.4 There are 2897 respondents with
complete records for all variables of interest in 1984.
The 1990 data set was constructed by imposing the same restrictions used to
create the 1984 data, with the exception of the age restriction. We did not impose an age
2 We also ran, but do not report, regressions that do not impose this restriction. The estimated
effects of the criminal-participation variables were slightly larger in these regressions.
3 Missing observations are those defined as REFUSAL, DON’T KNOW, INVALID SKIP, or NONINTERVIEWS. Variables also include the code VALID SKIPS, but this is not necessarily a missing observation. For example, VALID SKIPS for the variables ADLTCRT, NUMCHAR, and NUMCNVC reflect those not charged or convicted of crimes. These valid skips are recoded as zeros. This reduces the sample from 12,686 to 5,400. Of those remaining, 16.7% report having been charged with a crime and 9.9% report having been convicted.
4 This is done using a variable in the NLSY called Employment Status Recode (R15199), which reflects employment status during the week of the interview. Individuals coded “Going to School” were deleted.
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restriction for the 1990 sample because respondents to the survey were not of typical
school-going age. There are 3280 respondents with complete records for all variables of
interest in 1990. The 1990 sample is larger than the 1984 sample because the age
restriction was relaxed. We adjusted 1989 income data to constant 1983 dollars. Table 1
contains the summary statistics for the two samples.
III. Model
We estimate the model
( ) ititiiit VFCY εβββα ++++= 3201ln (1)
where itY is annual earnings in 1983 or 1989, 0iC is a set of criminal participation
variables for each person i , as of the interview year 1980, iF is a vector of fixed
individual characteristics, such as race, ethnicity, age and AFQT5 score, itV is a vector of
characteristics that vary over time, such as educational attainment, marriage, work
experience, union membership and whether one lives in a Metropolitan Statistical Area,
and itε is the individual-specific error term.
We use four alternative measures of youthful contact with the criminal-justice
system: (i) a dummy variable indicating whether the individual had been charged with a
crime; (ii) a dummy variable indicating whether the individual had been convicted of a
crime; (iii) a pair of dummy variables indicating, respectively, whether an individual had
been charged but not convicted, and whether he had been convicted; and (iv) a pair of
5 AFQT denotes the normalized score on the Armed Forces Qualification Test, administered in
1980 to over 90% of the NLSY panel, and measures pre-market skills.
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dummy variables denoting whether an individual’s criminal case was adjudicated in
juvenile or adult court. We estimate these four specifications for both the 1984 and 1990
samples, and therefore report eight sets of estimates on subsequent adult earnings.
Because characteristics that lead to high wages and employment also reduce
participation in criminal activity, estimates that do not control for this heterogeneity will
be biased toward finding the expected negative relationship – that youth criminal
participation leads to lower earnings. Several papers have attempted to control for
heterogeneity in a variety of ways. Grogger (1995) chose a comparison group for the
California arrestees comprising his sample to control statistically for any time-invariant,
individual-specific, unobservable characteristics. Waldfogel (1994b) and Lott (1992a,
1992b) estimated differences between pre- and post-conviction income as a function of
changes in criminal participation.
Unfortunately, because the NLSY records criminal participation only in the initial
year (1980), we do not observe changes in criminal participation, and cannot control for
unobserved heterogeneity with a fixed-effects, panel-data model. Instead we control for
heterogeneity in two ways. First, the NLSY contains an extensive set of demographic
variables that allow us to control for many observed individual characteristics. One of
these variables, AFQT, is frequently omitted from earnings regressions, and as a proxy
for ability captures much of the heterogeneity. Grogger (1995) pursued a similar strategy
by incorporating various demographic variables, but he excluded AFQT.6 Second, the full
model specification in (1) includes many characteristics over which individuals have
6 Grogger also notes a problem with the NLSY arrest data – blacks and whites have the same
number of self-reported arrests on average. In most other samples, however, the arrest rate for blacks is about 3 times that of whites.
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some degree of choice–these are captured in itV above. Because educational attainment,
marital status, and work experience are functions of criminal activity, the indirect effect
of youth criminal activity on adult earnings is absorbed by the coefficients on these
variables. Consequently, the estimate of 1β in the full specification understates the total
effect of youth criminal background on adult earnings.
Our analysis is limited to young adults who reported positive labor-market
earnings. However, both Freeman (1991) and Grogger (1992) found that having a
criminal record when young reduces the probability of legal employment as an adult.
Consequently, by restricting our sample to employed individuals, we further
underestimate the total effect of youth criminal background on adult earnings, inclusive
of its effect on employment status.
IV. Empirical Results
We begin our empirical analysis by estimating the raw, unadjusted difference in
adult earnings between individuals who, when young, had formal contact with the
criminal justice system (criminal charges and/or convictions) and those who did not. This
estimated difference does not control for either fixed, pre-market traits that affect adult
earnings (such as race or ability) or for other human-capital variables (like schooling and
work experience) that help determine adult earnings, but also could be affected by youth
criminal activity. We obtain this raw difference by estimating a bivariate regression in
which the dependent variable is either 1983 or 1989 log annual income.
Table 2 contains ordinary least-squares estimates of four bivariate regressions
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using 1983 log annual earnings as the dependent variable and each of the alternative
measures of youth criminal background. Column 1 indicates that individuals who were
charged with a crime when young (whether convicted) earned approximately 27% less in
1983, on average, than individuals who were not criminally charged. Of course, because
this regression does not control for observed (and unobserved) differences in
characteristics that affect earnings, this point estimate is equivalent to a simple
difference-in-means. The bivariate regression results reported in column 2 imply that
young adults convicted of a crime as youths earned about 29% less in 1983, on average,
than those who were not. As expected, the coefficient on having been convicted is larger
than the one on having been charged reported in column 1. Column 3 shows that those
youths who were charged but not convicted of a criminal offense earned approximately
21% less as young adults than individuals with no criminal charges against them, while
persons convicted of crimes when young earned about 31% less as young adults than did
those who had no criminal convictions. In column 4, finally, youths whose criminal
charges were adjudicated in juvenile and adult court experienced a 27% and 26%
decrease, respectively, in 1983 earnings compared with uncharged individuals.
Table 3 replicates the same four specifications for 1989 earnings, and shows the
same general results—the coefficients on the criminal sanction variables are uniformly
negative and significantly different from zero. The coefficient estimates on being charged
and convicted are slightly higher than for 1983 earnings.
An analysis of the effect of youth criminal background on adult earnings must
assign to (observable) pre-market characteristics some of the explanatory power for
differences in subsequent earnings between youthful offenders and non-offenders.
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Inherent skill (or ability or aptitude), along with ethnicity and age, are important
determinants of labor-market earnings that are unaffected by subsequent human-capital
investment but may be correlated with criminal behavior when young.
Tables 4 and 5 report least-squares estimates of the effect of our four alternative
measures of youth criminal activity, controlling for the pre-market variables, on 1983 and
1989 earnings, respectively. The point estimate in column 1 implies that, holding
ethnicity, skill, and age constant, individuals who were charged with a crime when young
earned almost 29% less in 1983 than those who were not. The magnitude of the
CHARGED coefficient is smaller in this specification than in the simple bivariate mode,
because in the latter, the estimated coefficient captures effects on subsequent earnings
more properly attributed to the pre-market variables included here. As expected, the
estimated coefficient on BLACK is negative and significantly different from zero, and
implies that blacks earn about 32% less than whites, holding pre-market skills and age
constant. However, this specification is extremely parsimonious, and does not control for
variables such as education and work experience that are typically included in earnings
regressions and are correlated with race. In contrast, the estimate of the HISPANIC
coefficient is small and not significantly different from zero. The estimated coefficients
on AFQT and AGE are positive and significantly different from zero, as expected.
Column 2 reports the results of estimating the same specification discussed above,
with criminal background now represented by a dummy variable indicating whether one
was convicted of a crime as a youth. The coefficient estimate on CONVICTED is
positive, significantly different from zero, and somewhat larger than the estimated
coefficient on the CHARGED variable reported in column 1. The estimated coefficients
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on the included pre-market variables are virtually identical to those in column 1.
Of course, individuals convicted of a crime when young were also charged with
that crime, so it is of interest to separate out the marginal effect on earnings of having
been convicted of a youthful crime, given that one has been charged with the crime. The
estimates in column 3 indicate that someone who was charged but not convicted earns
about 22% less than his uncharged counterpart. However, an individual who was charged
and subsequently convicted experienced a 34% reduction in 1983 labor-market earnings.
Therefore, the marginal impact of a prior conviction on 1983 earnings is about -11.5% [-
33.9 - (-22.4)], ceteris paribus.
Finally, the data permit us to distinguish between the subsequent earnings effects
of a criminal charge adjudicated in juvenile court rather than in adult court. Column 4
reports the empirical results for this specification, and shows that individuals whose
criminal cases were handled in juvenile court earned approximately 20% less than those
having had no contact when young with the criminal-justice system. However, those
youths whose cases were adjudicated in adult court experienced a 36% reduction in 1983
earnings. This large difference in coefficient estimates may reflect one or both of the
following phenomena: (i) because of the confidentiality of juvenile-court proceedings,
the “scarring” or “signaling” aspects of criminal charges handled in that setting are less
than in cases dealt with in open adult court; (ii) youths who commit crimes of such
severity that they are tried in adult court are different from their juvenile-court
counterparts in ways that adversely affect subsequent labor-market earnings. As before,
the 1989 results for the criminal sanction variables are very similar to the 1983 findings.
The results reported in Tables 4 and 5 control only for exogenous pre-market
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variables that, along with youth criminal background, affect the subsequent earnings of
young adults. However, the model on which these estimates are based is an under-
specified representation of the process determining such earnings. In particular, this
model specification excludes variables such as schooling and work experience which
proxy human-capital investment affecting earnings as a young adult. To redress this
shortcoming, we specify a more complete model of earnings incorporating additional
variables that are exogenous to earnings but whose values are determined by choices
made after adolescence.
Tables 6 and 7 report the results of this more completely specified earnings
model. Because we include both schooling and work experience in this regression and
use a sample of males for whom post-schooling work experience is, on average, highly
continuous, we excluded age from the estimated regressions. The estimated coefficients
on the pre-market variables HISPANIC and AFQT are very similar to those from the
more parsimonious specification reported in Table 4. Interestingly, the size of the
coefficient on BLACK is reduced by almost three-fifths after controlling for the post-
adolescence explanatory variables, suggesting considerable heterogeneity among the
black population with respect to these additional observable determinants of earnings.
The signs, sizes, and significance levels of the coefficients on the additional
explanatory variables in column 1 conform to standard results reported in the empirical
earnings literature. In particular, the coefficients on schooling (grades completed),
married, urban residence, and union membership are positive and significantly different
from zero. Additional weeks of work experience increase earnings, but at a decreasing
rate. Individuals who were charged with a crime when young earned approximately
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11.4% less in 1983 than their non-charged counterparts, and this adverse earnings effect
is significantly different from zero. However, the size of the criminal-background
discount on adult earnings is lowered by about three-fifths with the inclusion of
additional controls for observable influences on adult earnings. We interpret this
reduction in the estimated effect of youth criminal background to mean that a portion of
the total effect of having been charged when young with a criminal offense is now being
attributed to variables – such as labor-market experience and years of completed
schooling – that are affected by adolescent criminal activity. As a consequence, the
estimated coefficient on CHARGED is a downward-biased estimate of the true effect of a
youthful criminal charge on subsequent earnings. This downward bias offsets to an
unknown degree the upward bias in the estimated effect associated with any individual
heterogeneity arising from omitted (unobservable) variables that are correlated with both
youth criminal background and adult earnings.
In column 2 the point estimate of the CONVICTED coefficient is slightly higher
than that on CHARGED, reported in the previous column, and is significantly different
from zero. As before, the model specification in column 3 permits us to separate the
marginal effect of being convicted when young of a criminal offense from the effect of
having been charged but not convicted. The point estimates of the coefficients on both
criminal-participation variables are substantially lower than before, again suggesting that
the total effects of these variables are being attributed partly to post-adolescent individual
characteristics that are, in turn, affected by youth criminal behavior. The evidence from
this specification implies that an individual charged with a crime when young
experiences about a 9% reduction in earnings as a young adult, ceteris paribus, while the
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marginal effect on earnings of a conviction, having been charged, is -12.8 - (-8.8) = -
4.0%.
Column 4 reports the results of estimating the model with dummy variables
indicating adjudication of any criminal case(s) in adult or juvenile court. Again, the point
estimate of the coefficient on the adult-court variable is substantially lower than the
estimated coefficient on the juvenile-court variable (-0.131 versus -0.095). Moreover, the
magnitudes of both coefficients are lower in this estimated regression than in the more
parsimonious model reported in Table 4, as expected.
Compared with the 1983 results, the estimated effects on 1989 earnings of being
black, living in an urban area, being a union member, previous work experience, and
being married are smaller, while the estimated return to schooling is substantially larger.
The coefficient estimates on the variable CHARGED in column 1 across the three tables,
show essentially no difference in the magnitudes of the estimated effects on 1983 and
1989 earnings. The point estimate of the effect on 1989 earnings of having been
convicted is slightly higher than on 1983 earnings for each of the model specifications.
V. Conclusion
We have used data from a stratified random sample of young adults to estimate
the effect of youth criminal arrests, charges, and convictions on labor-market earnings as
an adult. Individuals charged with or convicted of a criminal offense when young have
lower adult earnings because they complete fewer years of schooling and accumulate less
work experience than those with no contact as a youth with the criminal-justice system.
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However, we show that youth criminal behavior when young also directly reduces adult
earnings, even after controlling for these human-capital variables. Having been charged
but not convicted decreases earnings by between 5-8% and having been convicted as a
youth permanently lowers adult earnings by at least 12%. Adjudication in a juvenile court
lowers adult earnings by at least 9%, while having one’s case adjudicated in an adult
court lowers earnings an additional 5%.
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References
Freeman, Richard (1991) “Crime and the Employment of Disadvantaged Youths.” NBER Working Paper no. 3875.
Grogger, Jeff (1992) “Arrests, Persistent Youth Joblessness, and Black/White Review of Economics and Statistics, Vol. 74
(February): 100-106.
Grogger, Jeff (1995) “Effect of Arrests on the Employment and Earnings of Young Quarterly Journal of Economics, Vol. 110 (February): 52-71.
Lott, John R. Jr. (1989) “The Effect of Conviction on the Legitimate Income of Economics Letters, Vol. 34, no. 4: 381-385.
Lott, John R. Jr. (1992a) “An Attempt at Measuring the Total Monetary Penalty from Drug Convictions: The Importance of an Individual’s Reputation.” Journal of Legal Studies, Vol. 21, (January): 159-187.
Lott, John R. Jr. (1992b) “Do We Punish High-Income Criminals Too Heavily?” Economic Inquiry, Vol. 30, (October): 583-608.
Nagin, Daniel and Joel Waldfogel (1995) “The Effects of Criminality and Conviction on the Labor Market Status of Young British Offenders.” International Review of Law and Economics, Vol. 15 (January): 109-126.
Nagin, Daniel, and Joel Waldfogel (1998) "The Effect of Conviction on Income Through the Life Cycle." International Review of Law and Economics, Vol. 18 (March): 25-40.
Waldfogel, Joel (1994a) “Does Conviction Have a Persistent Effect on Income and International Review of Law and Economics, Vol. 14 (March)
103-119.
Waldfogel, Joel (1994b) “The Effect of Criminal Conviction on Income and the Trust The Journal of Human Resources, Vol. 29, (Winter):
62-81.
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Table 1 Summary Statistics
Variable Number Mean St. Dev. Min. Max. 1984 Data
Age 2897 23.65 1.76 21 27 Income83 2897 11,237 8,210 25 75001 Black 2897 0.23 0.42 0 1 Hispanic 2897 0.14 0.35 0 1 AFQT89 2897 45.07 29.90 1 99 SMSA 2897 0.76 0.42 0 1 Grade 2897 12.46 2.14 2 20 Married 2897 0.33 0.47 0 1 Experience (weeks) 2897 206 77 2 312 Experience2 2897 48,452 29,837 4 97344 Union Member 2897 0.21 0.41 0 1 Charged 2897 0.18 0.39 0 1 Just Charged 2897 0.09 0.29 0 1 Convicted 2897 0.11 0.31 0 1 Adult Court 2897 0.10 0.29 0 1 Juvenile Court 2897 0.09 0.28 0 1
1990 data Age 3280 Income89 3280 17,963 12,133 40 138204 Black 3280 0.25 0.43 0 1 Hispanic 3280 0.16 0.36 0 1 AFQT89 3280 42.87 30.39 1 99 SMSA 3280 0.79 0.41 0 1 Grade 3280 12.92 2.47 3 20 Married 3280 0.52 0.50 0 1 Experience (weeks) 3280 435.31 132.20 3 624 Experience2 3280 206,963 107,009 9 389376 Union Member 3280 0.20 0.40 0 1 Charged 3280 0.14 0.35 0 1 Just Charged 3280 0.07 0.25 0 1 Convicted 3280 0.08 0.28 0 1 Adult Court 3280 0.06 0.24 0 1 Juvenile Court 3280 0.08 0.27 0 1
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Table 2 The Effect of Criminal Participation on 1983 Wages
Bivariate Regression (1) (2) (3) (4)
Variable Coeff. T-stat Coeff. T-stat Coeff. T-stat Coeff. T-stat Charged -0.268 -5.40 Convicted -0.288 -4.62 -0.308 -4.94 Just Charged -0.208 -3.09 Adult Court -0.263 -4.03 Juvenile Court -0.273 -3.97 Intercept 9.009 426.42 8.991 444.10 9.012 422.82 9.009 426.34 Num. of Obs. 2897 2897 2897 2897 2897 2897 2897 2897 F-Statistic Adj. R2
Notes: Dependent variable is the natural log of 1983 income. Standard errors are in parentheses.
Table 3 The Effect of Criminal Participation on 1989 Wages
Bivariate Regression (1) (2) (3) (4)
Variable Coeff. T-stat Coeff. T-stat Coeff. T-stat Coeff. T-stat Charged -0.279 -6.78 Convicted -0.325 -6.33 -0.339 -6.59 Just Charged -0.184 -3.25 Adult Court -0.220 -3.67 Juvenile Court -0.324 -6.11 Intercept 9.594 626.43 9.583 644.63 9.596 622.05 9.594 626.50 Num. of Obs. 3280 3280 3280 3280 3280 3280 3280 3280 F-Statistic Adj. R2
Notes: Dependent variable is the natural log of 1989 income.
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Table 4 The Effect of Criminal Participation on 1983 Wages
with Fixed Factors (1) (2) (3) (4)
Variable Coeff. T-stat Coeff. T-stat Coeff. T-stat Coeff. T-stat Charged -0.286 -5.99 Convicted -0.314 -5.26 -0.339 -5.64 Just Charged -0.224 -3.49 Adult Court -0.362 -5.76 Juvenile Court -0.202 -3.06 Black -0.322 -6.50 -0.315 -6.36 -0.324 -6.56 -0.324 -6.56 Hispanic 0.030 0.54 0.028 0.50 0.026 0.47 0.026 0.47 AFQT 0.121 5.83 0.124 5.99 0.119 5.73 0.119 5.73 Age 0.119 11.27 0.119 11.24 0.120 11.39 0.120 11.39 Intercept 6.267 25.04 6.251 24.93 6.242 24.94 6.242 24.94 Num. of Obs. 2897 2897 2897 2897 2897 2897 2897 2897 F-Statistic Adj. R2
Notes: Dependent variable is the natural log of 1983 income. Standard errors are in parentheses.
Table 5 The Effect of Criminal Participation on 1989 Wages
with Fixed Factors (1) (2) (3) (4)
Variable Coeff. T-stat Coeff. T-stat Coeff. T-stat Coeff. T-stat Charged -0.271 -7.11 Convicted -0.322 -6.81 -0.336 -7.08 Just Charged -0.160 -3.08 Adult Court -0.308 -5.48 Juvenile Court -0.245 -5.02 Black -0.162 -4.73 -0.158 -4.61 -0.163 -4.76 -0.162 -4.74 Hispanic 0.061 1.61 0.059 1.54 0.058 1.53 0.061 1.59 AFQT 0.265 17.89 0.268 18.19 0.265 17.88 0.265 17.89 Age 0.047 7.94 0.045 7.69 0.047 7.93 0.0485 7.98 Intercept 8.571 64.65 8.596 64.90 8.575 64.73 8.551 63.65 Num. of Obs. 3280 3280 3280 3280 3280 3280 3280 3280 F-Statistic Adj. R2
Notes: Dependent variable is the natural log of 1989 income.
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Table 6 The Effect of Criminal Participation on 1983 Wages
Full Specification (1) (2) (3) (4)
Variable Coeff. T-stat Coeff. T-stat Coeff. T-stat Coeff. T-stat Charged -0.114 -2.73 Convicted -0.117 -2.26 -0.128 -2.46 Just Charged -0.088 -1.58 Adult Court -0.131 -2.41 Juvenile Court -0.095 -1.66 Black -0.125 -2.81 -0.123 -2.77 -0.126 -2.82 -0.125 -2.81 Hispanic -0.024 -0.50 -0.024 -0.51 -0.025 -0.53 -0.024 -0.51 AFQT 0.124 5.42 0.123 5.36 0.123 5.40 0.124 5.43 SMSA 0.149 3.98 0.145 3.86 0.148 3.94 0.149 3.97 Grade 0.017 1.73 0.019 1.95 0.017 1.74 0.017 1.73 Married 0.325 9.40 0.324 9.38 0.325 9.40 0.325 9.40 Experience 0.012 12.47 0.012 12.44 0.012 12.48 0.012 12.47 Experience2 0.000 -6.40 0.000 -6.35 0.000 -6.41 0.000 -6.40 Union 0.282 7.18 0.284 7.25 0.282 7.18 0.281 7.18 Intercept 6.783 44.47 6.751 44.65 6.783 44.47 6.782 44.46 Num. of Obs. 2897 2897 2897 2897 2897 2897 2897 2897 F-Statistic Adj. R2
Notes: Dependent variable is the natural log of 1983 income. Standard errors are in parentheses.
Table 7 The Effect of Criminal Participation on 1989 Wages
Full Specification (1) (2) (3) (4)
Variable Coeff. T-stat Coeff. T-stat Coeff. T-stat Coeff. T-stat Charged -0.117 -3.39 Convicted -0.140 -3.28 -0.145 -3.36 Just Charged -0.049 -1.04 Adult Court -0.148 -2.99 Juvenile Court -0.093 -2.09 Black -0.091 -2.86 -0.090 -2.83 -0.091 -2.86 -0.091 -2.85 Hispanic 0.037 1.09 0.037 1.07 0.037 1.06 0.037 1.08 AFQT 0.129 7.36 0.129 7.35 0.130 7.37 0.130 7.38 SMSA 0.119 4.10 0.115 3.94 0.116 3.97 0.119 4.09 Grade 0.061 9.35 0.062 9.56 0.061 9.39 0.061 9.35 Married 0.228 9.26 0.229 9.32 0.228 9.28 0.228 9.26 Experience 0.006 11.93 0.006 11.83 0.006 11.84 0.006 11.96 Experience2 0.000 -7.58 0.000 -7.49 0.000 -7.50 0.000 -7.60 Union 0.161 5.49 0.161 5.49 0.161 5.49 0.161 5.50 Intercept 7.019 56.38 7.011 56.43 7.025 56.24 7.014 56.31 Num. of Obs. 3280 3280 3280 3280 3280 3280 3280 3280 F-Statistic Adj. R2
Notes: Dependent variable is the natural log of 1989 income.
