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The Absorbing Status of Incarceration and its Relationship with Wealth Accumulation

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Abstract

Objectives This study extends our knowledge on the negative effects of incarceration to the accumulation of wealth by examining whether, how, and how much incarceration affects home ownership and net worth. It also investigates how these outcomes vary with the time since a person was incarcerated and the number of incarceration periods, along with addressing potential mechanisms behind this relationship. Methods I apply hybrid mixed effects models that disaggregate within- and between person variation to investigate incarceration’s relationship with home ownership and net worth, using National Longitudinal Study of Youth data from 1985 to 2008. I also incorporate a set of mediation models in order to test for indirect effects of incarceration on wealth through earnings, health, and family formation. Results My results show that incarceration limits wealth accumulation. Compared to never-incarcerated persons, ex-offenders are less likely to own their homes by an average of 5 percentage points, and their probability of home ownership decreases by an additional 28 percentage points after incarceration. Ex-offenders’ net worth also decreases by an average of $42,000 in the years after incarceration. Conclusions When combined with previous research on incarceration, my findings show that incarceration acts as an absorbing status, potentially leading to the accumulation of disadvantage. Although incarceration’s negative effects on wealth accumulation were partially mediated by its relationship with earnings and family formation, incarceration directly affected home ownership and net worth. In most cases, former inmates began with flatter wealth trajectories and experienced additional losses after incarceration.
ORIGINAL PAPER
The Absorbing Status of Incarceration and its
Relationship with Wealth Accumulation
Michelle Lee Maroto
Published online: 22 August 2014
!Springer Science+Business Media New York 2014
Abstract
Objectives This study extends our knowledge on the negative effects of incarceration to
the accumulation of wealth by examining whether, how, and how much incarceration
affects home ownership and net worth. It also investigates how these outcomes vary with
the time since a person was incarcerated and the number of incarceration periods, along
with addressing potential mechanisms behind this relationship.
Methods I apply hybrid mixed effects models that disaggregate within- and between
person variation to investigate incarceration’s relationship with home ownership and net
worth, using National Longitudinal Study of Youth data from 1985 to 2008. I also
incorporate a set of mediation models in order to test for indirect effects of incarceration on
wealth through earnings, health, and family formation.
Results My results show that incarceration limits wealth accumulation. Compared to
never-incarcerated persons, ex-offenders are less likely to own their homes by an average
of 5 percentage points, and their probability of home ownership decreases by an additional
28 percentage points after incarceration. Ex-offenders’ net worth also decreases by an
average of $42,000 in the years after incarceration.
Conclusions When combined with previous research on incarceration, my findings show
that incarceration acts as an absorbing status, potentially leading to the accumulation of
disadvantage. Although incarceration’s negative effects on wealth accumulation were
partially mediated by its relationship with earnings and family formation, incarceration
directly affected home ownership and net worth. In most cases, former inmates began with
flatter wealth trajectories and experienced additional losses after incarceration.
Electronic supplementary material The online version of this article (doi:10.1007/s10940-014-9231-8)
contains supplementary material, which is available to authorized users.
M. L. Maroto (&)
Department of Sociology, University of Alberta, 6-23 Tory Building, Edmonton, AB T6G 2H4,
Canada
e-mail: maroto@ualberta.ca
123
J Quant Criminol (2015) 31:207–236
DOI 10.1007/s10940-014-9231-8
Keywords Incarceration !Stratification !Cumulative disadvantage !Wealth !Home
ownership
Introduction
Criminologists and sociologists have highlighted the role of incarceration in reproducing
inequality in employment, education, voting, and health (Wakefield and Uggen 2010). As a
stigmatized legal status, incarceration hinders an offender’s re-integration into society and
often becomes a primary status for the evaluation of that person. In addition, state and
federal laws limit access to voting rights, employment, and social services for previously
incarcerated persons (Manza and Uggen 2006; Samuels and Mukamel 2004). Due to the
incarceration of numerous young, minority, and low-income men in the United States, the
lingering negative effects of incarceration disproportionately harm members of these
groups (Western 2006). As a result, incarceration often exacerbates broader societal
inequalities through its negative social status.
The status of an ‘‘ex-offender’’ or ‘‘former prisoner’’ results not only from individual
involvement in criminal activity, but also from the criminal justice system’s varying
enforcement efforts and responses to charges, which often depend on the crime, its loca-
tion, and the characteristics of the defendant and the victim (Pettit and Western 2004;
Wacquant 2001; Western 2006). Once the status of ex-offender is achieved, however, this
status, often referred to as a criminal credential, can lead to long lasting negative conse-
quences for former offenders (Pager 2003,2007; Wakefield and Uggen 2010). With such
negative effects, a previous incarceration can become an overarching absorbing status and
the basis for the accumulation of disadvantage over time.
In view of the growing body of research on the contribution of incarceration to
inequality, this paper examines whether, how, and how much incarceration affects two
indicators of wealth: home ownership and net worth. The persistent negative effects of
incarceration in multiple spheres likely spread to areas of wealth accumulation. This can
then influence personal wellbeing because wealth confers a variety of advantages,
including better neighborhoods, social and cultural capital, and political sway (Bricker
et al. 2012; Bucks 2012; Keister 2000a; Keister and Moller 2000). Even a modest amount
of wealth can create a safety net for households in times of financial distress (Spilerman
2000).
The many advantages of wealth, along with its unequal distribution in the United States,
make it a key site for inequality where wealth disparities remain across many groups today
(Bricker et al. 2012; Bucks 2012; Keister 2000a). With their limited access to employment,
former prisoners may also face similar barriers to wealth accumulation. Even though it is
likely that former prisoners will hold little wealth due to their multiple disadvantaged
statuses, we have no research that demonstrates the presence of a relationship between
incarceration and wealth accumulation. I seek to remedy this omission by analyzing the
association between incarceration and the outcomes of home ownership and net worth
using data from the National Longitudinal Study of Youth 1979 cohort.
I begin this paper by drawing on theories of ascription, stigma, and cumulative
advantage/disadvantage as well as Pager’s (2003,2007) concept of the criminal credential
in order to introduce incarceration as an absorbing status. I continue by discussing the
negative effects of incarceration and my reasons for expecting a link between incarceration
208 J Quant Criminol (2015) 31:207–236
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and wealth. After describing my measures and methods, which include hybrid mixed
effects models, I present findings showing that incarceration is associated with a reduced
probability of home ownership and lower net worth. Because I investigate change over
time and the potential mechanisms that contribute to incarceration’s relationship with
wealth accumulation, a feat that few incarceration studies have accomplished, these
findings also demonstrate how the negative effects of incarceration extend into this new
area through multiple pathways.
The Status of Incarceration and its Negative Effects
Wakefield and Uggen (2010, 388) described current and former prisoners as a ‘‘Weberian
status group sharing similar life chances determined by a common and consequential mark
of [dis]honor.’’ This mark of dishonor potentially lasts indefinitely and can outweigh other
statuses that a person may prefer to identify with. Incarceration, therefore, acts as a
stigmatized status that is formalized through a criminal record or ‘‘credential’’ (Pager
2007). As a result, incarceration can become part of a process of ascription, where roles are
assigned and resources are allocated based upon categorical group membership (Kemper
1974; Mayhew 1968).
Incarceration as a Criminal Credential
In defining characteristics as reference points for ascription, researchers often refer to the
difference between distributing resources based on personal attributes acquired at birth
versus individual performance that can change over time. In reality, this ascription-
achievement dichotomy is not always so straightforward where multiple characteristics
have been conceptualized as both achieved and ascribed (Cadge and Davidman 2006;
Jacobsen and Kendrick 1973; Lorber 1994). The status of an ‘‘ex-convict,’’ ‘‘ex-felon,’’ or
‘formerly incarcerated person’’ falls somewhere in between this dichotomy of status
acquisition categories, which is encompassed by Pager’s (2007) concept of a criminal
credential that limits access to opportunity.
According to Pager (2007), credentials represent formalized status distinctions that can
be used to define legal rights or barriers, which legitimizes their use for the distribution of
resources. Like a college degree, a criminal credential certifies an individual’s position in
society, but unlike a degree, this ‘‘earned’’ credential comes with legal restrictions and a
stigma that brands ex-offenders as untrustworthy (Pager 2007; Pettit and Lyons 2007). A
criminal credential is achieved once a person is incarcerated for his or her criminal
behavior and then released. However, after a person acquires this status, it becomes a
lasting marker and a normatively acceptable basis for unequal treatment in a society that
purports to treat people as equals. A previous incarceration then becomes a status that
works ascriptively by determining the distribution of resources.
Incarceration as a Stigmatized Status
Of course, incarceration is more than an achieved status; it is a stigmatized status. As a
stigma, or according to Goffman (1963, 3) ‘‘an attribute that is deeply discrediting,’
incarceration acts differently than a credential. The term ‘‘credential’’ generally implies the
receipt of a positive status, one that a person would choose to share with others. As
undesirable characteristics, stigmas are not markers that people choose; stigmas are
J Quant Criminol (2015) 31:207–236 209
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imposed upon people, usually on people with less power. Stigmas can play a critical role
within the process of stratification, particularly when they are used to justify the differ-
ential treatment of groups (Link and Phelan 2001).
Like ascription, stigma encompasses a power component. As Link and Phelan (2001,
377) noted: ‘‘stigma exists when elements of labeling, stereotyping, separation, status loss,
and discrimination occur together in a power situation that allows them.’’ Current and
former prisoners typically come from groups who already lack power, which facilitates
passing along this mark of dishonor. Members of less powerful groups are at higher risk of
incarceration than others with far reaching effects, which is reflected by the composition of
the prison population in the United States (Western 2002,2006). Prisoners in the United
States tend to come from the most disadvantaged groups—groups whose members lack
social, economic, and political power (Wakefield and Uggen 2010; Western 2006). They
are young, from low-income families, and average less schooling than a high school
degree. The prison population also disproportionately comprises racial and ethnic minor-
ities.
1
It is likely then that the continuing negative effects of incarceration will compound
these race and class inequalities, exacerbating the disadvantage of former prisoners.
Absorbing Statuses and the Accumulation of Disadvantage
In order to better describe the status of former prisoners, I borrow the term ‘‘absorbing
state’’ from the literature on social mobility, which uses Markov chain models to represent
the relationship between social structure and social mobility (Henry et al. 1971; Matras
1967; McGinnis 1968). In these models, the axiom of cumulative inertia predicts that the
probability of remaining in a state will increase with the time spent in that state, to the
point that some states become finite absorbing states (Henry et al. 1971; McGinnis 1968).
2
When applied to certain characteristics, this perspective highlights how even achieved
statuses can become lasting markers that act as if they were ascribed. The negative effects
of incarceration show that it can become an absorbing status that is used ascriptively across
multiple spheres of life, leading to a process of accumulating disadvantage.
According to cumulative advantage/disadvantage theory, achieved and ascribed statuses
can have persisting effects in returns to resources, leading to diverging outcomes in which
some people continually build their resources relative to others. This theory originated with
Merton’s (1968,1988) work on recognition in the scientific community. Since then
researchers have applied aspects of cumulative advantage theory to various arenas,
including crime and delinquency (DiPrete and Eirich 2006; Sampson and Laub 1997). I use
the term accumulating disadvantage broadly to explain the persisting effects of a previous
incarceration on a person’s credit market outcomes, similar to Lyons and Pettit’s (2011)
use of compounded disadvantage. The negative status of ‘‘ex-convict’’ reflects incarcera-
tion’s lingering effects that compound any prior disadvantaged status.
1
In 2011 the incarceration rate for black non-Hispanic males ranged between 5 and 9 times that of white
non-Hispanic males, depending on the age group, and the rate for Hispanic males was two to three times that
of white non-Hispanic males (Carson and Sabol 2012). The largest disparities occurred for younger age
groups.
2
In the vacancy chain literature, a vacancy is created when a new resource unit enters a population and an
individual leaves his or her unit behind to take that new position (Chase 1991; White 1970). This move
initiates a sequence of moves as other individuals in the chain transfer into the vacant units. Within a
vacancy chain an absorbing state acts as the end state or the termination of a chain.
210 J Quant Criminol (2015) 31:207–236
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Incarceration’s Far Reaching Effects
The many negative effects of incarceration support a process of cumulative disadvantage
that continues after prisoners, who already come from disadvantaged groups, are released.
Labor market barriers disadvantage ex-convicts and often lead to segmented labor market
access for those with a previous incarceration on their record (Piore 1970; Western and
Beckett 1999). Research on labor market outcomes using longitudinal data and adminis-
trative records has consistently shown lower employment rates and earnings for previously
incarcerated persons, particularly black men (Apel and Sweeten 2010; Freeman 1992,
1996; Kling 2006; Lott 1990; Nagin and Waldfogel 1998; Waldfogel 1994; Western 2002;
Western and Beckett 1999; Western and Pettit 2000). Even though wage trajectories begin
to recover over time, many years after they are released, previously incarcerated persons
earn less and spend more time without employment than persons who have never been to
prison (Pettit and Lyons 2007; Western 2002; Western and Beckett 1999). Moreover, the
effects of incarceration on labor market outcomes are much greater than the penalties for
more limited interactions with the criminal justice system, such as arrests and convictions
(Western et al. 2001).
In addition to affecting employability and earnings, incarceration reduces men’s like-
lihood of marriage and places those who do marry at a higher risk of divorce while they are
incarcerated, but not always after incarceration (Apel et al. 2010; Lopoo and Western
2005; Wildeman and Muller 2012). Although they often depend on a family’s situation
prior to a paternal incarceration, the consequences of incarceration extend to other family
members as well; they often lead to psychological issues for children, in addition to family
poverty (Turney and Wildeman 2013; Wildeman 2009,2010). Inmates have also been
shown to experience illness, depression, stress, and other psychological problems at dis-
proportionately higher rates than the larger population (Massoglia 2008a,b). These effects
continue and can even worsen long after incarceration (Schnittker and John 2007;
Schnittker et al. 2011). Despite a pressing need for social services and aid, former prisoners
in many states find that their criminal credential restricts their ability to obtain state support
(Samuels and Mukamel 2004). In view of these negative effects and added restrictions, it
seems likely that incarceration could further disadvantage former prisoners in their ability
to accumulate wealth.
Incarceration and Wealth
I investigate a potential consequence of incarceration that has not been studied: whether
incarceration limits wealth accumulation. With its basis in various types of property
ownership, wealth shapes people’s economic and personal wellbeing, creates more stability
than income, and provides benefits that extend to many areas (Keister 2000a; Spilerman
2000). For example, home ownership can provide access to better neighborhoods and
school systems, assets can increase social networks, and the ownership of various goods
heightens social status (Keister 2000a; Spilerman 2000).
Due to these advantages, I focus on wealth accumulation and examine the effects of a
previous incarceration on home ownership and net worth. Specifically, my research
addresses the following questions: How does incarceration affect the accumulation of
wealth for formerly incarcerated individuals? Does the time since a person was incarcer-
ated influence these outcomes, as a process of accumulating disadvantage implies? Do
J Quant Criminol (2015) 31:207–236 211
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these outcomes vary by the length of an incarceration or the number of incarceration
periods? Finally, what are the potential mechanisms behind this relationship? This mark
has been shown to shape labor market outcomes, and I expect that a previous incarceration
will present a negative association with a person’s probability of home ownership and
accumulation of net worth through multiple related pathways.
Disadvantage for previously incarcerated persons operates through numerous
mechanisms. These include selection barriers, such as ex-offenders’ limited educa-
tion, skills, and work experience; ex-offenders’ physical, mental, drug, and motiva-
tional problems; discrimination by employers and other gatekeepers; and the
stigmatization of formerly incarcerated persons (Holzer et al. 2003;Pager2007;
Western et al. 2001). Although secondary data analyses seldom address which of
these mechanisms furthers disadvantage, audit studies emphasize the barriers gen-
erated through employer discrimination that ex-offenders face in the labor market.
Audit studies of employers in Milwaukee and New York showed that employers were
one-half to one-third less likely to consider an ex-offender for an open position than
an equally qualified person without a criminal credential, controlling for race (Pager
2003,2007;Pageretal.2009).
The individual and structural factors that limit the employability of previously incar-
cerated persons could also affect their ability to accumulate wealth, particularly if, like
employers, lenders interpret a previous incarceration as a signal of untrustworthiness or
instability (Holzer 1996; Holzer et al. 2003; Pager and Quillian 2005). In this case, a
previous incarceration would limit access to lending, a general requirement for wealth
building, but incarceration affects wealth in other ways as well. Recent research on legal
financial obligations (LFOs) demonstrates how the criminal justice system imposes added
debt burdens on offenders through the use of heavy pre- and post-conviction fines and fees
that ex-offenders often cannot afford to pay (Harris et al. 2010,2011). Furthermore,
incarceration limits an individual’s ability to make payments, which could lead to debt
delinquency, negative reports from collection agencies, and limitations on future lending.
Thus, I expect that the stigmatized status of incarceration will directly limit wealth
accumulation for previously incarcerated persons. I also expect that these effects will
worsen over time and with additional periods of incarceration, as former prisoners fall
farther behind. With these expectations, I expand on Harris et al. (2010) research to
demonstrate the broader implications of incarceration for wealth accumulation beyond fees
imposed by the legal system.
Because it acts as an absorbing status that affects multiple areas of life, incarceration
should also limit wealth accumulation through multiple pathways or mechanisms. Sev-
eral of these mechanisms operate with respect to a person’s absence from the labor
market. For instance, increased earnings are positively associated with home ownership
and wealth accumulation (Keister and Moller 2000). The lost work experience that
incarceration imposes and its consequences for earnings later on can also impede a
person’s ability to accumulate assets and develop a credit history. Because family for-
mation often leads to wealth accumulation and home ownership (Bricker et al. 2012),
incarceration should affect wealth through its relationship with marriage. Finally, the
added problems created by the health limitations of formerly incarcerated persons should
also affect wealth accumulation. Overall, I expect that ex-offenders’ stigmatization, lost
earnings, limited marriage prospects, and added health limitations will all lead to post-
incarceration wealth reductions.
212 J Quant Criminol (2015) 31:207–236
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Data
I use the 1979 cohort of the National Longitudinal Study of Youth (NLSY79) to estimate
the effects of incarceration on credit market outcomes.
3
The NLSY79 cohort is a stratified
multistage sample of 12,686 men and women who were between 14 and 22 years old when
first surveyed in 1979.
4
Respondents were interviewed annually until 1994, after which
they were interviewed every 2 years. The survey is ongoing with debt and asset data most
recently collected in 2008.
My data take the form of an unbalanced panel sample in which the number of time
periods may differ across individuals. I restrict my sample to 1985 through 2008 and I
exclude data from 1991, 2002, and 2006 because the NLSY did not consistently collect
data on assets and debt over time. After removing years when wealth questions were not
asked, individuals from the military oversample, observations with missing data, and
extreme outliers on net worth, a sample of 10,274 individuals (or cases) and 96,180
observations (or person-years) remained. My sample therefore covers over 90 percent of
respondents from the original 11,406 individuals who were not a part of the military
subsample.
Methods
For my analyses, I use hybrid mixed effects regression models that include fixed effects for
time-varying covariates and random effects for time-invariant covariates, which allow me
to disaggregate within- and between-person variation in the same models (Allison 2009).
These models take the form of random effects models—also known as multilevel varying-
intercept, mixed, and hierarchical models— that account for correlated disturbance terms
for the same person over time by assigning each person a separate intercept (Allison 2009;
Gelman and Hill 2007). I incorporate fixed effects for time-varying covariates within each
model by expressing these variables as deviations from their person-specific means, which
represent the within-person variation across time periods (Allison 2009). I also include the
person-specific means in order to provide estimates based on the average between-person
variation.
By incorporating the deviations from person-specific means for time-varying covariates
along with the person-specific means into each model, I am able to discuss the variation of
a particular score for an individual in the sample (or the average of the average change in a
score for an individual over time) as well as the variation in the average score across
individuals. This allows me to control for unobserved, stable, time-invariant, individual-
level characteristics while assessing the effects of time-varying and time-invariant factors
(Allison 2009). Hybrid mixed effects models, therefore, help to overcome the limitations of
3
In using this longitudinal survey I expand upon the research of Freeman (1992), Western and Beckett
(1999), Western (2002), and the more recent work of Massoglia et al. (2013). These studies applied fixed
effects models to NLSY data to investigate the effects of incarceration on labor market outcomes.
4
The original sample comprised a cross-sectional sample of 6,111 respondents, a supplemental sample of
5,295 respondents that oversampled civilian Hispanic, black, and economically disadvantaged non-black/
non-Hispanic youth, and a military sample of 1,280 respondents (NLSY79 User’s Guide). I use the full
dataset except for the military sample in my analyses to observe as many incarcerations as possible. I also
include a variable in all models to indicate whether the respondent was a member of the cross-sectional
sample.
J Quant Criminol (2015) 31:207–236 213
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separate fixed and random effects models, yet they provide estimates consistent with both
procedures.
5
Equation 1represents the general random effects model for continuous data, which I
use to estimate an individual’s net worth at time, t,
6
yit ¼ltþbXit þcZiþaiþeit ð1Þ
where iindexes the individual respondent and tindexes yearly observations per individual.
In this equation, ltrepresents the time-varying intercept, cZirepresents vectors of the time-
invariant coefficients and predictor variables, bXit represents vectors of the time-varying
coefficients and predictor variables, and eit is the error term that represents random vari-
ation at each point in time. These models assume that ai, which is treated as a set of
random variables with a specified probability distribution, is independent of all other
variables in the model.
I then partition the within- and between-person variation through person-specific mean-
centering of the time-varying predictors in Eq. 1(Allison 2009; Curran and Bauer 2011).
The process of person-specific mean-centering controls for unobserved heterogeneity in
time-varying covariates by decomposing these variables into their within- and between-
person variation. Equation 2illustrates this process,
x&
it ¼xit 'xið2Þ
where x&
it represents the person-mean centered time-varying covariate, xit is the original
score for individual iat time t, and xirepresents the person-specific mean for individual i. I
include both components in the models, and report the coefficients for within-person and
between-person variation separately. I also report random effects coefficients for time-
invariant covariates in the model. Although I report coefficients for both levels, I primarily
discuss the within-person effects in my results. Equation 3summarizes this full model,
which now includes coefficients and vectors for X&
it and Xi.
yit ¼ltþbX&
it þðc1Xiþc2ZiÞþaiþeit ð3Þ
I also incorporate an autoregressive disturbance term to account for the correlation of
error terms within persons over time. The error term is described by Eq. 4:
eit ¼qeit;t'1þgit ð4Þ
where q
jj
\1 and git is independent and identically distributed with mean 0 and variance
h2
g.
I include a set of mediation models in order to also test for indirect relationships
between incarceration and wealth at both the within- and between-person levels. The most
common method for calculating indirect effects involves estimating two equations for the
outcome variable, one that includes the mediator and one that does not, and then finding
the difference in coefficients for the initial variable across the two equations (Krull and
5
Although the coefficient estimates are consistent with both procedures, they are not identical. In particular,
the fixed effects coefficients vary from the within-person coefficients because my data are unbalanced.
Random effects coefficients differ from the between-person coefficients because random effects models do
not purely rely on between-person variation. They also consider some within-person variation.
6
I use a logit form of this equation to estimate an individual’s probability of home ownership at time, t.
214 J Quant Criminol (2015) 31:207–236
123
MacKinnon 2001).
7
However, this method often produces biased estimates for non-linear
models, including logit models with binary outcomes, because the coefficients and error
variance are not separately identified (MacKinnon and Dwyer 1993). In order to estimate
indirect effects and overcome these biases, I apply Karlson et al. (2011) KHB-method that
accounts for model rescaling (Breen et al. 2013; Karlson and Holm 2011). This allows me
to then discuss both the direct and indirect effects of incarceration on wealth accumulation.
Measures
Outcome Variables
My first outcome variable is home ownership, that is, whether the respondent owns or
makes payments on his or her dwelling. Most families’ major sources of wealth are their
homes, and home ownership provides benefits that include residential stability, tax breaks,
and access to schooling (Keister 2000a; Shapiro 2004; Spilerman 2000). Moreover, home
ownership entails access to mortgage lending, which generally requires both an income and
a strong credit history. As illustrated by Table 1, the gap in home ownership rates by
incarceration status is obvious; 24 percent of ever-incarcerated respondents owned their
homes in 2008 compared to 67 percent of never-incarcerated respondents.
I use a measure of net worth in 2010 dollars as my second outcome variable. The survey
calculates net worth by subtracting the respondent’s total debts from the total value of all
assets.
8
Using wealth data as my outcome variable creates certain limitations. As Spiler-
man (2000) noted, wealth figures in representative surveys can be inconsistent due to the
complexity of wealth, a lack of standardization across surveys, and the difficulty many
respondents have in estimating their wealth. In order to address these limitations, I exclude
extreme outliers on these variables and remove cases with missing data.
9
I also limit my
analysis to variables consistently collected in the survey. Similar to home ownership,
Table 1shows a gap of approximately $173,000 in net worth by incarceration status. The
disparity is smaller in terms of median net worth; the median net worth of ever-incar-
cerated respondents in 2008 was $507 compared to $92,000 for never-incarcerated
respondents.
7
The basic ‘‘product-of-coefficient’’ method is another popular way to estimate indirect effects (Bauer et al.
2006; Krull and MacKinnon 2001; Zhang et al. 2009). Estimates obtained using the additive and the
product-of-coefficient methods are usually equivalent for linear outcomes in single-level models, but they
often diverge in multilevel and nonlinear models (Krull and MacKinnon 2001; MacKinnon and Dwyer
1993).
8
The NLSY calculates net worth with the following equation: NET WORTH =HOME VALUE -
MORTGAGE -PROPERTY DEBT ?CASH SAVING ?STOCKS/BONDS ?TRUSTS ?BUSINESS
ASSETS -BUSINESS DEBT ?CAR VALUE -CAR DEBT ?POSSESSIONS -OTHER
DEBT ?IRAs ?401Ks ?CDs. Net worth also includes the respondent’s spouse’s assets and debt. In
order to account for this, I include marital status as a control variable in all full models. I also estimated
additional models for a subset of respondents who were never married, and I tested for interactions between
incarceration and marital status. In these models, the effects of incarceration remained statistically signif-
icant and negative.
9
I removed cases with a total net worth greater than $2 million or less than -$2 million 2010 USD. This
removed 313 cases.
J Quant Criminol (2015) 31:207–236 215
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Table 1 Descriptive statistics of NLSY79 variables for most recent survey year (2008)
Full sample Never incarcerated Ever incarcerated
N individuals 6,033 5,678 355
Home ownership
a
64.51 67.07 23.66
Net worth (dollars) $203,824 $213,991 $41,205
Ever incarcerated 5.88 * *
Currently incarcerated 0.51 * *
Incarcerated in past year 0.86 * *
Incarcerated 1–5 years ago 0.50 * *
Incarcerated 6–10 years ago 1.66 * *
Incarcerated more than 10 years ago 2.87 * *
Incarcerated for 1 survey year 2.45 * *
Incarcerated for 2–4 survey years 1.99 * *
Incarcerated for 5 or more survey years 1.44 * *
Age (years) 46.63 46.63 46.54
Employment status
Employed full-time 66.83 68.25 44.23
Employed part-time 12.71 12.79 11.55
Unemployed 3.63 3.17 10.99
Out of the labor force 16.82 15.80 33.24
Job gaps since age 18
No job gaps 27.18 28.11 12.39
Single job gap 21.68 22.14 14.37
Two?job gaps 51.14 49.75 73.24
Self-employed 9.56 9.30 13.80
Government employee 16.77 17.45 5.92
Individual earnings (dollars) $39,713 $41,080 $17,842
Years of schooling completed (years) 13.31 13.39 11.90
Marital status
Married 54.14 56.02 23.94
Never married 17.77 16.43 39.15
Formerly married 28.10 27.54 36.90
Any children 56.46 58.42 25.07
Health limitation 14.88 14.11 27.32
Rural 22.61 23.05 15.49
Female 52.13 54.63 12.11
Black
b
31.03 29.46 56.06
Hispanic origin
b
18.47 18.39 19.72
AFQT Score (percentile) 38.91 40.16 18.95
Any drug use in teens 70.13 68.95 89.01
Marijuana use in teens 69.45 68.28 88.17
Cocaine use in teens 17.79 16.77 34.08
Other drug use in teens 16.72 16.13 26.20
Source: NLSY 1979 Cohort, full sample
a
Estimates refer to percentages, unless otherwise noted
b
Because I analyze the full NLSY79 sample, blacks and Hispanics are overrepresented in these data
216 J Quant Criminol (2015) 31:207–236
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Predictor Variables
My primary predictor variable measures the respondent’s incarceration status as whether
the respondent was previously incarcerated prior to the current time period. The NLSY
provides information on incarceration through two measures. The 1980 survey, which
included an extensive set of questions related to illegal activity, asked respondents if and
when they were incarcerated. The NLSY also records a respondent’s residence each year,
including whether the person resided in a jail or prison at the time of the interview.
10
I use
these two measures to create a variable that indicates whether the respondent was ever
incarcerated as an adult over 18 years of age at each time point. To ensure that my
estimates of the effects of incarceration apply only to people who were previously
incarcerated, I control for whether the respondent was incarcerated at the current survey
wave. As Table 1shows, approximately six percent of NLSY respondents had been
incarcerated by 2008.
I also test two categorical variables that encompass different aspects of incarceration
because the effects of incarceration on wealth accumulation can vary based upon the
timing of incarceration, as well as the length and number of incarceration spells. Therefore,
in order to estimate the accumulating effects of incarceration over time, I created a cat-
egorical variable that measures a respondent’s time since incarceration. This variable has
six categories: never incarcerated, currently incarcerated, incarcerated in the past year,
incarcerated one to 5 years ago, incarcerated 6–10 years ago, and incarcerated more than
10 years ago. In addition, I created a categorical variable to measure the effects of multiple
incarcerations and the length of an incarceration. This variable has five categories: never
incarcerated, currently incarcerated, previously incarcerated for one survey-year, incar-
cerated for 2–4 survey-years, or incarcerated for five or more survey-years.
11
Time-Varying Covariates
I include time-varying covariates for demographic, employment, health, family, and
regional variables commonly used in studies that predict wealth and earnings (Keister and
Moller 2000; Kenworthy 2007). I control for an individual’s labor market situation by
including the respondent’s employment status, cumulative job gaps, and earnings. I cate-
gorize employment status as employed full-time (35 or more hours per week), employed
part-time (less than 35 h per week), unemployed, and out of the labor force, with indi-
viduals employed full-time as the referent category. I measure a respondent’s cumulative
number of job gaps lasting 8 weeks or more using a categorical variable with the following
categories: no job gaps, a single job gap, and multiple job gaps. I use the respondent’s
earnings in thousands of 2010 dollars, unless noted otherwise. I also include measures that
indicate whether the respondent was employed in a government job or was self-employed.
10
Because interviews record whether the respondent was incarcerated only at the time of the interview, my
measure of incarceration misses persons who were not imprisoned at the time of the interview, but spent
some time in prison during that year.
11
Because the survey reports whether the respondent was incarcerated only at the time of each interview, I
am not able to determine whether being incarcerated for multiple survey-years refers to a single multi-year
incarceration spell or multiple separate incarceration spells. For example, a respondent who was incarcer-
ated for two survey-years could have been incarcerated two separate times for a few months that coincided
with when the interview occurred, or the respondent could have been incarcerated for two full years. Either
situation would place this respondent in the category of incarcerated for two to four survey-years.
J Quant Criminol (2015) 31:207–236 217
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I control for the respondent’s age, education, marital status, presence of children, and
health limitations. Because the age of this sample is truncated at 51 years, I expect a
positive effect of age on wealth, but I also include a quadratic age-squared term in order to
account for any non-linear relationships (Dynan and Kohn 2007; Modigliani 1986).
12
I
measure education as the number of completed years of schooling because that is how the
NLSY collected education information. Marital status indicates whether the respondent
was married, formerly married (separated, divorced, or widowed), or never married. I treat
any children and health limitations as binary variables. Marriage, the presence of children,
and added education should increase wealth, but marriage dissolution and health limita-
tions should have the opposite effect (Bricker et al. 2012; Dynan and Kohn 2007; Smith
1999).
Time-Invariant Covariates
I include time-invariant covariates for the respondent’s sex, race, AFQT score, and teenage
drug use, covariates that do not change over time and that could affect a respondent’s
likelihood of incarceration and his or her ability to accumulate wealth. Incarceration rates
differ greatly by a person’s race and sex (BJS, 2010; Carson and Sabol 2012; Western
2006), as does wealth accumulation (Keister 2000b; Krivo and Kaufman 2004). I therefore
measure sex as a dichotomous variable of male or female and race as two dichotomous
variables, black and Hispanic origin.
AFQT score refers to the respondent’s Armed Forces Qualification Test percentile
score, which the survey collected for each respondent in 1980. Although AFQT score has
been a controversial measure of intelligence, it offers a standardized measure of cognitive
aptitude and school-based knowledge (Farkas 2003; Maume et al. 1996; U.S. Department
of Defense 1982). I also include three binary variables that indicate whether the respondent
reportedly used marijuana,cocaine, or other drugs as a teenager, which can relate to a
person’s level of self control that influences employment and potentially wealth accu-
mulation (Gottfredson and Hirschi 1990). The category of other drugs includes heroin,
psychedelics, inhalants, and any other drugs.
In addition to these time-invariant covariates, I also include indicator variables to
designate the cross-sectional sample and survey wave year. The cross-sectional sample
variable indicates whether the respondent was a member of the original cross-sectional
sample, where the referent category refers to members of this sample. The survey-year
variable is an indicator variable with 14 categories where the referent is the most recent
wave (2008). I include this variable to account for any unobserved period effects.
Findings
Incarceration was negatively associated with both measures of wealth accumulation across
analyses. Formerly incarcerated individuals generally had lower average wealth, as mea-
sured in terms of home ownership and net worth, than individuals who had never been to
prison. Moreover, formerly incarcerated persons averaged less wealth in the years after
incarceration compared to the years before. In most cases, former inmates began with
flatter wealth trajectories, partly due to their already disadvantaged statuses, lower levels of
education, and limited earnings, and they experienced additional losses after incarceration.
12
Age also acts as a proxy for time or year because the NLSY is a longitudinal cohort sample.
218 J Quant Criminol (2015) 31:207–236
123
My findings therefore emphasize how prior disadvantages are then compounded by
incarceration.
Home Ownership
Net of time-invariant individual characteristics accounted for by within-person fixed
effects coefficients, formerly incarcerated persons were less likely to be homeowners in the
years after incarceration compared to the years before. As illustrated by the between-
person random effects coefficients that controlled for earnings, employment, family, and
education, ever-incarcerated persons were also less likely than similar never-incarcerated
persons to own a home at any point between 1985 and 2008. These results appear in
Table 2, which uses whether the respondent was previously incarcerated to predict home
ownership.
All models in Table 2include time-invariant covariates, but in Models 1–3, I sequen-
tially added certain time-varying covariates in order to observe changes in the effects of a
previous incarceration on home ownership. Model 1 controls for only age and its quadratic
term, Model 2 includes family and health covariates, and Model 3 (the full model) includes
controls for employment and earnings. Adding controls decreased the magnitude of the
coefficients for incarceration, but across all models a previous incarceration showed a
significant negative association with home ownership, as evidenced by the coefficients
representing within- and between-person variation. This means that, on average, the
probability of home ownership was lower for people who experienced incarceration, and,
among those who were incarcerated at one point, the average probability of home own-
ership decreased in the years after being incarcerated.
Net of control variables, the average probability of home ownership was only about 5
percentage points lower for respondents with a previous incarceration when compared to
similar never-incarcerated individuals. Moreover, for those who were incarcerated, the
average probability of home ownership decreased by approximately 28 percentage points
in the years after incarceration, an estimate that corresponds to the within-person coeffi-
cient for incarceration in Model 3.
13
It is also important to note that these estimates do not
include currently incarcerated individuals, who experienced even larger declines in the
probability of home ownership.
As seen in Model 3, most time-varying and time-invariant control variables were sta-
tistically significant and associated with home ownership in the expected direction. Mar-
riage, the presence of children, employment, and earnings were all positively associated
with home ownership. Age showed a curvilinear relationship with home ownership, in
which its effects decreased over time. As expected from the literature on race and wealth,
blacks and Hispanics had a lower average probability of home ownership than other racial
groups. Additionally, most teenage drug use variables were not significantly associated
with later home ownership, except for cocaine use, which showed a negative association.
Finally, AFQT scores had a positive association, albeit a small one.
13
These estimates represent the effects of covariates at the mean of the data as determined by the intercept,
which gives the predicted probability of home ownership when all variables are held at their means for
continuous variables and referent categories for categorical variables. This estimate specifically applies to
the 2008 survey wave because that is the referent category for the survey wave year variable. To obtain the
upper bound of the predictive difference we can also divide the coefficient by four to approximate the
difference at which the slope of the logistic curve is maximized (Gelman and Hill, 2007). Doing so provides
the values of -0.14 and -0.34 for the between- and within-person coefficients for previous incarceration.
J Quant Criminol (2015) 31:207–236 219
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Table 2 Results from hybrid logistic mixed models predicting home ownership
Model 1 Model 2 Model 3
b SE b SE b SE
Intercept 2.135*** (.326) 3.006*** (.316) 2.505*** (.317)
Time-varying predictors
Within-person coefficients
Currently incarcerated -1.865*** (.218) -1.440*** (.227) -1.092*** (.230)
Previously incarcerated -1.887*** (.178) -1.611*** (.189) -1.375*** (.193)
Age .156*** (.018) .151*** (.017) .156*** (.017)
Age squared -.009*** (.000) -.006*** (.000) -.005*** (.000)
Marital status (ref: married)
Never married -2.358*** (.061) -2.297*** (.061)
Formerly married -2.251*** (.046) -2.270*** (.046)
Any children .860*** (.042) .883*** (.042)
Any health limitation -.321*** (.057) -.144* (.059)
Rural .354*** (.048) .368*** (.049)
Employment status (ref:
employed full-time)
Employed part-time -.137*** (.039)
Unemployed -.170** (.066)
Out of the labor force -.207*** (.052)
Job gaps (ref: no job gaps)
Single job gap -.417*** (.089)
2?job gaps -.859*** (.110)
Total earnings (in 1,000s) .017*** (.001)
Self-employed .168** (.066)
Government employee .151** (.056)
Years of schooling .010 (.026)
Between-person coefficients
Currently incarcerated -4.005*** (.658) -1.918*** (.574) -.567 (.571)
Previously incarcerated -1.585*** (.248) -1.016*** (.223) -.571** (.221)
Age .205*** (.014) .149*** (.013) .131*** (.013)
Age squared -.008*** (.002) -.008*** (.001) -.007*** (.001)
Marital status (ref: married)
Never married -4.445*** (.105) -4.047*** (.103)
Formerly married -4.427*** (.119) -4.064*** (.117)
Any children .285** (.094) .565*** (.094)
Any health limitation -1.399*** (.177) -.324 (.184)
Rural 1.177*** (.088) 1.299*** (.087)
Employment status (ref:
employed full-time)
Employed part-time -.475** (.164)
Unemployed -1.729*** (.292)
Out of the labor force -.429** (.153)
Job gaps (ref: no job gaps)
220 J Quant Criminol (2015) 31:207–236
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To better understand the effects of incarceration on home ownership, Table 3displays
the results from models that considered the time since a person was incarcerated and the
length of incarceration. The timing and length of incarceration showed a stronger and
more consistent association with home ownership when comparing individuals before
and after incarceration, as opposed to comparing average outcomes for previously and
never-incarcerated persons. In addition, the negative effects of incarceration on home
ownership seem to increase over time, as a theory of accumulating disadvantage would
predict.
As shown in Table 3Model 1, the effects of incarceration on home ownership increased
over time, but not by much. Most of the between-person coefficients were not significantly
different from zero. In terms of the within-person coefficients, the gap in the predicted
probability of home ownership was greatest for currently incarcerated individuals and
persons incarcerated over ten years ago, at 21 and 25 percentage points. This indicates that
the negative effects of incarceration continue well after a person is released. Model 2,
which partitions incarceration based on the number of survey-years for which the
respondent had been incarcerated, shows fairly consistent disparities in home ownership,
except for respondents who were incarcerated for more than 4 survey-years. However, the
currently incarcerated population likely influences this outcome because over half of all
currently incarcerated observations at time twere incarcerated for more than four survey-
years.
Table 2 continued Model 1 Model 2 Model 3
b SE b SE b SE
Single job gap -.442*** (.080)
2?job gaps -.741*** (.072)
Total earnings (in 1,000s) .024*** (.002)
Self-employed .556** (.188)
Government employee .306** (.114)
Years of schooling .035* (.017)
Time-invariant predictors
Female .345*** (.064) .128* (.062) .656*** (.067)
Black -2.143*** (.099) -.639*** (.094) -.696*** (.094)
Hispanic origin -1.124*** (.106) -.451*** (.099) -.510*** (.097)
AFQT Score .022*** (.001) .019*** (.001) .008*** (.001)
Used marijuana in teens -.243*** (.072) -.029 (.065) .018 (.064)
Used cocaine in teens -.549*** (.093) -.216** (.084) -.224** (.082)
Used other drugs in teens -.350*** (.092) -.149 (.083) -.052 (.081)
AIC 77,344.38 67,680.80 66,413.14
BIC 77,638.08 68,069.23 66,972.10
Rho .70 .63 .62
Analyses are for the sample of all individuals. Continuous variables (earnings, age, work experience, grade,
and AFQT score) are grand mean centered. All models include covariates for the cross sectional sample and
survey wave year. Within-person coefficients approximate fixed effects. Between-person coefficients
approximate random effects
Source: NLSY 1979 Cohort, 1985–2008. N =10,274 individuals and 96,180 observations
*** p\.001, ** p\.01, * p\.05
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Net Worth
Table 4, which presents the results from hybrid mixed effects regression models predicting
total net worth, shows that the effects of incarceration on wealth accumulation extend
beyond home ownership. Although the interpretation varied when comparing between- and
within-person coefficients, net of stable unobserved individual characteristics and of
Table 3 Results from hybrid logistic mixed models predicting home ownership based on the time since
incarceration and the length of incarceration
Model 1 Model 2
b SE b SE
Intercept 2.522*** (.317) 2.510*** (.317)
Time-varying predictors
Within-person coefficients
Incarceration status (ref: never incarcerated)
Currently incarcerated -1.618*** (.267)
Incarcerated in past year -1.074*** (.312)
Incarcerated 1–5 years ago -1.201*** (.246)
Incarcerated 6–10 years ago -1.234*** (.249)
Incarcerated 11 or more years ago -1.849*** (.282)
Incarceration length/times (ref: never incarcerated)
Currently incarcerated -1.592*** (.279)
Incarcerated for 1 survey year -1.348*** (.246)
Incarcerated for 2–4 survey years -1.265*** (.297)
Incarcerated for 5 or more survey years -1.095* (.429)
Between-person coefficients
Incarceration status (ref: never incarcerated)
Currently incarcerated -1.011 (.531)
Incarcerated in past year .011 (.404)
Incarcerated 1–5 years ago -.656 (.708)
Incarcerated 6–10 years ago -1.255 (.783)
Incarcerated 11 or more years ago -.234 (.703)
Incarceration length/times (ref: never incarcerated)
Currently incarcerated -1.296* (.586)
Incarcerated for 1 survey year -.561* (.260)
Incarcerated for 2–4 survey years -.838 (.460)
Incarcerated for 5 or more survey years .914 (.916)
AIC 66,422.42 66,426.31
BIC 67,038.23 67,023.17
Rho .62 .61
Analyses are for the sample of all individuals. Models include all covariates from Model 3 in Table 2.
Continuous variables (earnings, age, grade, and AFQT score) are grand mean centered. Within-person
coefficients approximate fixed effects. Between-person coefficients approximate random effects
Source: NLSY 1979 Cohort, 1985–2008. N =10,274 individuals and 96,180 observations
*** p\.001, ** p\.01, * p\.05
222 J Quant Criminol (2015) 31:207–236
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observed control variables that change over time, incarceration was also associated with a
decrease in net worth.
As seen in Model 1, when controlling for only age and time-invariant covariates,
previously incarcerated individuals had an average net worth that was $28,000 less than
that of never-incarcerated individuals, and, for those with an incarceration, their net worth
was about $67,000 less in the years after incarceration compared to the years before. This
disparity declined, but still remained statistically significant, after controlling for family
and health aspects in Model 2. When I incorporated additional controls in Model 3, the
between-person coefficient was no longer associated with net worth, indicating that dif-
ferential rates of employment, earnings, and home ownership explain much of the average
disparity in net worth across never- and formerly-incarcerated individuals. However, the
within-person coefficient for incarceration showed that, even after controlling for labor
market aspects and home ownership—by far the largest contributor to wealth—net worth
decreased by approximately $42,000 in the years after incarceration.
Most time-varying and time-invariant covariates were significantly related to the out-
come variable in the expected direction. For example, marriage, earnings, education, and
home ownership were positively associated with between- and within-respondent net worth
disparities over time. In terms of time-invariant covariates, blacks and Hispanics held
lower net worth than non-blacks and non-Hispanics, which demonstrates the continuing
gaps in racial wealth. However, there were several variables that did not affect net worth in
the expected direction. In this model, use of cocaine as a teenager was positively associated
with net worth, although this variable was negatively related to home ownership in
Table 2. Unemployment also showed a positive relationship with net worth, but this
association was positive only in models that controlled for income and education, as Model
3 does.
Replicating the analysis for home ownership, Table 5presents models that consider the
time since a person was incarcerated (Model 1) and the length of incarceration (Model 2).
Interestingly, the between-person coefficients in this table indicate that the timing and
length of incarceration were not associated with average net worth between ever- and
never-incarcerated persons, but the within-person coefficients show that, net of stable
unobserved individual-level characteristics, the negative effects of incarceration grew over
time and with the length of incarceration. For instance, formerly incarcerated persons had a
net worth of approximately $105,000 less, on average, in the years where they were
incarcerated 11 or more years ago, when compared to the years prior to their incarceration.
However, the gap for those incarcerated 1–5 years ago was about $45,000. In terms of the
length of incarceration, having spent 5 or more survey-years in prison was associated with
a $71,000 decrease in net worth, compared to the years prior to incarceration, a value more
than double that of those who were incarcerated for only one survey-year. These results
therefore partially support my expectation that incarceration’s negative effects on net
worth would increase over time and with the length of incarceration.
Potential Mechanisms
Isolating the effects of any single variable is a complex process, particularly in the case of
incarceration because it affects so many aspects of a person’s life. It decreases education
and employment opportunities, impedes family formation, and leads to physical and
psychological health limitations (Wakefield and Uggen 2010). Incarceration is also
implicated in the broader U.S. race discrimination system (Reskin 2012). Incarceration,
therefore, acts a cause and consequence of multiple aspects within my models, which
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Table 4 Results from hybrid linear mixed models predicting net worth
Model 1 Model 2 Model 3
b SE b SE b SE
Intercept 171,681.9*** (13,278.1) 217,913.2*** (13,346.0) 50,529.0*** (12,581.1)
Time-varying predictors
Within-person coefficients
Currently incarcerated -18,048.7*** (5,432.1) -13,892.3** (5,417.8) -8,955.9 (5,439.2)
Previously incarcerated -67,473.6*** (7,691.8) -60,082.8*** (7,586.4) -41,505.4*** (7,343.7)
Age 3,840.2*** (731.0) 3,327.4*** (715.6) 3,027.3*** (653.7)
Age squared 40.2 (24.2) 110.8*** (24.0) 158.1*** (23.0)
Marital status (ref: married)
Never married -36,316.4*** (2,474.6) -19,700.2*** (2,452.5)
Formerly married -39,561.8*** (2,163.4) -26,214.1*** (2,148.2)
Any children 22,991.0*** (1,850.5) 17,317.5*** (1,820.1)
Any health limitation -10,991.6*** (2,127.8) -8,487.4*** (2,120.7)
Rural 12,715.5*** (2,101.7) 10,561.7*** (2,060.1)
Employment (ref: employed full-time)
Employed part-time 14,164.0*** (1,344.0)
Unemployed 14,400.1*** (2,033.7)
Out of the labor force 18,807.7*** (1,891.4)
Job gaps (ref: no job gaps)
Single job gap -5,882.0 (3,971.0)
2?job gaps -19,404.1*** (4,959.4)
Total earnings (in 1,000s) 951.2*** (30.3)
Self-employed 25,200.7*** (2,560.5)
Government employee -1,628.9 (2,142.5)
Years of schooling 4,376.8*** (1,187.4)
224 J Quant Criminol (2015) 31:207–236
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Table 4 continued
Model 1 Model 2 Model 3
b SE b SE b SE
Home ownership 58,083.4*** (1,572.2)
Between-person coefficients
Currently incarcerated 6,418.0 (20,383.0) 18,778.8 (19,780.4) -3,494.5 (17,987.9)
Previously incarcerated -28,011.3** (9,061.9) -17,072.8* (8,791.0) 974.6 (7,734.8)
Age 2,203.4*** (548.1) 2,192.9*** (547.7) 94.6 (492.9)
Age squared 30.3 (66.2) 28.9 (64.3) 60.4 (57.7)
Marital status (ref: married)
Never married -79,404.7*** (4,402.8) -15,194.8*** (4,318.8)
Formerly married -101,487.8*** (5,064.9) -26,967.4*** (4,913.1)
Any children -24,653.5*** (4,105.4) -28,338.1*** (3,696.8)
Any health limitation -44,046.5*** (7,351.8) -27,534.0*** (6,971.2)
Rural -15,461.6*** (3,873.3) -10,318.1** (3,457.6)
Employment (ref: employed full-time)
Employed part-time 59,447.3*** (6,436.7)
Unemployed 63,720.0*** (10,413.6)
Out of the labor force 120,419.1*** (5,934.6)
Job gaps (ref: no job gaps)
Single job gap -3,408.8 (3,173.4)
2?job gaps -5,361.1 (2,864.9)
Total earnings (in 1,000s) 2,558.7*** (75.4)
Self-employed 111,233.1*** (7,459.4)
Government employee -13,859.1** (4,548.4)
Years of schooling 5,495.8*** (658.8)
Home ownership 121,660.6*** (4,377.3)
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Table 4 continued
Model 1 Model 2 Model 3
b SE b SE b SE
Time-invariant predictors
Female -3,631.2 (2,611.2) 2,425.1 (2,724.3) 19,904.8*** (2,648.6)
Black -37,235.5*** (3,949.3) -22,057.9*** (4,040.5) -16,817.7*** (3,640.4)
Hispanic origin -10,399.8* (4,294.2) -9,729.8* (4,310.8) -6,250.1 (3,789.4)
AFQT Score 1,576.7*** (51.9) 1,288.1*** (52.4) 174.7** (57.6)
Used marijuana in teens -3,084.7 (2,921.7) 787.5 (2,845.2) 1,074.1 (2,494.8)
Used cocaine in teens 5,490.2 (3,732.7) 8,117.9* (3,622.7) 7,304.5* (3,161.0)
Used other drugs in teens -20,428.3*** (3,754.6) -15,516.9*** (3,637.1) -8,206.4** (3,177.6)
AIC 2,562,063 2,560,636 2,555,441
BIC 2,562,375 2,561,044 2,556,038
Rho .69 .67 .65
Analyses are for the sample of all individuals. Continuous variables (earnings, age, work experience, grade, and AFQT score) are grand mean centered. All models include
covariates for the cross sectional sample and survey wave year. Within-person coefficients approximate fixed effects. Between-person coefficients approximate random effects
Source: NLSY 1979 Cohort, 1985–2008. N =10,274 individuals and 96,180 observations
*** p\.001, ** p\.01, * p\.05
226 J Quant Criminol (2015) 31:207–236
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complicates my ability to extract specific mechanisms, but it also supports incarceration’s
role as an absorbing stigmatized status.
In the case of wealth accumulation, other areas, such as the family or labor market, can
act as mechanisms that influence home ownership and net worth for former prisoners.
Tables 2and 4show that multiple covariates influence the relationship between incar-
ceration and wealth accumulation, as the coefficients for incarceration decreased with the
addition of controls across Models 1–3. Due to its absorbing status, it is likely that
incarceration affects other family, employment, and health variables, which in turn
influence net worth and home ownership. Therefore, in order to parse out some of these
potential pathways, I also tested for mediating effects with marital status, earnings, health,
and home ownership for a previous incarceration.
14
The model results that appear in Tables 6and 7show that the total effect of incar-
ceration on wealth outcomes is much greater than just its direct effect, once indirect effects
are also considered. These tables present coefficients for a previous incarceration’s total
and direct effects on home ownership (Table 6) and net worth (Table 7), as well as the
indirect effects of a previous incarceration on the outcome variables through the listed
mediating variables. In addition to coefficients, I also include estimates of the relative
percentage of the total effect explained by the decomposed relationships.
Except for the between-person effects for net worth, the direct effects of incarceration
accounted for the majority of its effects on wealth, but they played a larger role in
explaining home ownership than net worth. The indirect effects through marriage, earn-
ings, and health limitations explained 14 percent of the within-person effects and 23
percent of the between-person effects for home ownership. These variables, along with
home ownership, accounted for 35 percent of the within-person effects and 99 percent of
the between-person effects for net worth.
Marriage was a strong and consistent predictor of home ownership and net worth in all
models (Tables 2,4). Marriage also mediated the relationship between incarceration and
home ownership (Table 6), but not between incarceration and net worth (Table 7).
Incarceration presented additional indirect effects on home ownership and net worth via
earnings, which was positively associated with home ownership and net worth in all cases
(Tables 2,4). However, earnings was a much stronger mediating variable for the rela-
tionship between incarceration and net worth, than for home ownership, accounting for
17–40 percent of the total effects in terms of net worth. Finally, although health limitations
generally showed a negative association with wealth outcomes, the indirect effects related
to a previous incarceration were negligible. Overall, though, health limitations may not be
the best measure of health status because they often refer to certain disabilities, not specific
illnesses or diseases.
Home ownership, as one of the major sources of wealth for most people, was by far the
strongest mediator between incarceration and net worth (Table 7). This relationship likely
explains many of the non-significant effects of incarceration on net worth in Model 3 in
Table 4, which controls for home ownership. In addition, the indirect effects of incar-
ceration on net worth through home ownership were very large, in terms of both within-
and between-person variation.
In summary, the mechanisms behind these incarceration effects resemble those implied
by labor market research, which include ex-offenders’ limited education, skills, and work
experience and their physical, mental, drug, and motivational problems (Holzer 1996;
14
To simplify this set of analyses, I coded marital status as currently married or not currently married for
the mediation models.
J Quant Criminol (2015) 31:207–236 227
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Holzer et al. 2003; Pager 2007). I control for many of these mechanisms by including
certain covariates in my models and by focusing on within-person variation over time. I
also test for indirect relationships between marriage, earnings, health limitations, and
wealth outcomes. I find that, by reducing ex-offenders’ labor market experience and
Table 5 Results from hybrid linear mixed models predicting net worth based on the time since incarcer-
ation and the length of incarceration
Model 1 Model 2
b SE b SE
Intercept 51,982.5*** (12,583.2) 50,511.9*** (12,580.1)
Time-varying predictors
Within-person coefficients
Incarceration status (ref: never incarcerated)
Currently incarcerated -22,964.2** (7,751.9)
Incarcerated in past year -18,948.5 (9,703.1)
Incarcerated 1–5 years ago -45,296.5*** (9,492.1)
Incarcerated 6–10 years ago -75,595.6*** (10,561.7)
Incarcerated 11 or more years ago -104,552.8*** (12,455.0)
Incarceration length/times (ref: never
incarcerated)
Currently incarcerated -35,612.3*** (7,691.9)
Incarcerated for 1 survey year -33,041.1*** (9,389.2)
Incarcerated for 2–4 survey years -52,949.9*** (9,908.6)
Incarcerated for 5 or more survey years -71,173.9*** (12,956.9)
Between-person coefficients
Incarceration status (ref: never incarcerated)
Currently incarcerated -1,517.3 (16,309.7)
Incarcerated in past year -2,383.1 (14,132.5)
Incarcerated 1–5 years ago 47,740.5 (24,518.1)
Incarcerated 6–10 years ago -18,299.0 (29,256.6)
Incarcerated 11 or more years ago 485.7 (25,520.3)
Incarceration length/times (ref: never
incarcerated)
Currently incarcerated 2,732.9 (17,814.4)
Incarcerated for 1 survey year 1,245.8 (9,471.4)
Incarcerated for 2–4 survey years 6,677.3 (14,810.4)
Incarcerated for 5 or more survey years -18,902.2 (30,508.9)
AIC 2,555,401 2,555,437
BIC 2,556,055 2,556,071
Rho .65 .65
Analyses are for the sample of all individuals. Models include all covariates from Model 3 in Table 4.
Continuous variables (earnings, age, work experience, grade, and AFQT score) are grand mean centered.
Within-person coefficients approximate fixed effects. Between-person coefficients approximate random
effects
Source: NLSY 1979 Cohort, 1985–2008. N =10,274 individuals and 96,180 observations
*** p\.001, ** p\.01, * p\.05
228 J Quant Criminol (2015) 31:207–236
123
earnings, as well as their marriage prospects, incarceration limits their opportunities to
accumulate wealth. Incarceration’s effects on wealth are also related, where home own-
ership mediates its relationship with net worth.
Beyond these mechanisms and mediating relationships, current and previous incarcer-
ations were directly associated with wealth outcomes. This occurs in part because time in
prison reduces a person’s opportunity to accumulate wealth and debt by removing him or
her from society. In this situation, growing legal debt, hiatuses, and periods without
spending money, concomitants of incarceration, can negatively affect future wealth
accumulation. In addition, the fact that conviction and incarceration leave a ‘‘record’
means that the absorbing status of incarceration confers a stigma that restricts a person’s
ability to re-enter society and start over. In view of evidence that discrimination and the
general stigmatization of formerly incarcerated persons limit their labor market outcomes
(Pager 2003,2007), it follows that incarceration could directly diminish wealth accumu-
lation through similar pathways.
Discussion
My findings further confirm incarceration’s continuing negative effects on people’s post-
incarceration lives. In addition to employment disadvantages, previously incarcerated
Table 6 Results from hybrid mixed effects mediation models predicting home ownership
Within-person Between-person
Estimate SE Estimate SE
Coefficients
Total effect -1.600*** (.193) -.759*** (.220)
Direct effect -1.376*** (.193) -.571*** (.221)
Indirect effects -.225*** (.031) -.187*** (.032)
Via marriage -.084*** (.027) -.128*** (.029)
Via earnings -.138*** (.013) -.047*** (.009)
Via health limitation -.002 (.002) -.012 (.007)
Relative percentage
Direct effect 85.97 76.92
Indirect effects 14.03 23.08
Via marriage 5.24 16.83
Via earnings 8.64 6.25
Via health limitation .15 1.61
Analyses are for the sample of all individuals. Models include all covariates from Model 3 in Table 2.
Within-person coefficients approximate fixed effects. Between-person coefficients approximate random
effects. The total effect refers to the effect of the initial variable on the outcome controlling for everything
except the mediator variables. The direct effect refers to the effect of the initial variable on the outcome
variable controlling for the mediator variables. The indirect effect refers to the effect of the initial variable
on the outcome variable mediated by the control variables. Indirect effects were calculated using the KHB-
method (Breen et al. 2013). Standard errors and significance tests for the indirect effects were calculated
according to Sobel’s (1982) method. Relative percentage refers to the percentage of the total effect
accounted for by the coefficient
Source: NLSY 1979 Cohort, 1985–2008. N =10,274 individuals and 96,180 observations
J Quant Criminol (2015) 31:207–236 229
123
persons also face disadvantages in accumulating wealth, an area of growing interest for
researchers (Turney and Schneider 2014). My findings show that, in most models, people
who had spent some time in prison were less likely to own their homes and often accu-
mulated less wealth than similar individuals who had never been incarcerated, net of
unobserved individual aspects and other time-varying and time-invariant indicators. More
importantly, formerly incarcerated persons were less likely to be homeowners and held less
wealth on average in the years after incarceration than in the years prior to their
incarceration.
Beyond illustrating this basic relationship, my findings highlight many of the nuances in
incarceration’s association with wealth accumulation. This relationship was largely med-
iated by the effects of incarceration on marriage, and earnings, as well as by home own-
ership in the case of net worth. Across models, I also allowed the effects of incarceration
on home ownership and net worth to vary based on the person’s time since incarceration
and length of incarceration. These findings showed that the effects of the timing and length
of incarceration did not differ between never- and ever-incarcerated persons, net of control
variables. In terms of within-person variation, persons incarcerated longer ago and for
more survey-years had lower rates of home ownership and levels of net worth, which is
consistent with the accumulation of disadvantage.
Table 7 Results from hybrid mixed effects mediation models predicting net worth
Within-person Between-person
Estimate SE Estimate SE
Coefficients
Total effect -64,923.36*** (5,683.50) -11,323.05 (6,968.31)
Direct effect -41,902.01*** (5,693.84) -87.69 (6,975.08)
Indirect effects -23,021.36*** (1,336.75) -11,235.36*** (1,202.46)
Via marriage -886.58** (291.31) -364.29* (142.43)
Via earnings -10,822.37*** (887.50) -4,524.54*** (773.94)
Via health limitation -262.82* (129.72) -935.52*** (254.90)
Via home ownership -11,049.58*** (809.27) -5,411.01*** (760.00)
Relative percentage
Direct effect 64.54 .77
Indirect effects 35.46 99.23
via marriage 1.37 3.22
via earnings 16.67 39.96
Via health limitation .40 8.26
Via home ownership 17.02 47.79
Analyses are for the sample of all individuals. Models include all covariates from Model 3 in Table 4.
Within-person coefficients approximate fixed effects. Between-person coefficients approximate random
effects. The total effect refers to the effect of the initial variable on the outcome controlling for everything
except the mediator variables. The direct effect refers to the effect of the initial variable on the outcome
variable controlling for the mediator variables. The indirect effect refers to the effect of the initial variable
on the outcome variable mediated by the control variables. Indirect effects were calculated using the KHB-
method (Breen et al. 2013). Standard errors and significance tests for the indirect effects were calculated
according to Sobel’s (1982) method. Relative percentage refers to the percentage of the total effect
accounted for by the coefficient
Source: NLSY 1979 Cohort, 1985–2008. N =10,274 individuals and 96,180 observations
230 J Quant Criminol (2015) 31:207–236
123
Through the use of hybrid mixed effects models, this paper also offers a methodological
contribution to research on the consequences of incarceration. Hybrid mixed effects
modeling helps to isolate the effects of incarceration because it generates estimates that
control for all unobserved time-invariant individual-level characteristics, as well as esti-
mates that can be used to compare average differences across similar individuals and deal
with issues of selection into incarceration. These models therefore address the common
concerns about identifying the effects of incarceration when it is often correlated with other
disadvantaged statuses. With these methods, I was also able to estimate coefficients for
time-invariant covariates that include measures for sex, race, and, at least crudely, for past
drug use and cognitive ability. Thus, incarceration was related to a person’s wealth net of
his or her earnings, age, education, race, or sex, key variables that also affect a person’s
probability of incarceration.
Because I do not have counterfactual cases (i.e., outcomes for identical persons who had
never been incarcerated) with whom I can compare wealth outcomes, I cannot truly infer
causality in my findings. Issues of dynamic selection may affect these results where the
time-varying factors that influence an individual’s likelihood of incarceration can also
shape his or her ability to accumulate wealth (Bjerk 2009). In order to address this issue, I
conducted a series of sensitivity analyses using data from the NLSY 1997 cohort to look at
how arrests, convictions, and incarceration periods connect to wealth outcomes (see the
Supplemental Appendix for analyses). I could not employ as rigorous models with these
data due to the age of the cohort, whose members were born between 1980 and 1984, and
the collection of wealth information, which occurs at five-year intervals. However, I was
able to compare wealth outcomes for respondents at age 25 based upon multiple types of
interactions with the criminal justice system, including arrests, convictions, and incar-
ceration periods. Across models with a variety of controls, arrests and convictions were
often negatively associated with asset and debt outcomes, but previous incarcerations
generally presented much larger negative associations in these data. Thus, when combined
with these sensitivity analyses, my results strongly suggest that incarceration directly and
indirectly limits wealth accumulation more so than other sanctions.
My findings are robust and stable across various NLSY79 samples as well. In addition
to my analyses of the full NLSY79 sample, I estimated the same models using the cross-
sectional sample with and without sampling weights and using full samples where I
employed multiple imputation procedures to account for missing data (see the Supple-
mental Appendix for analyses). The general rates of home ownership, levels of net worth,
and rates of incarceration varied across these samples, but the results for these models were
analogous to those from the full complete-case sample. Incarceration presented a consistent
negative association with wealth outcomes that varied by the timing and length of
incarceration.
Despite my use of multiple models and data samples, the structure of the data and the
measurement of incarceration still impose limitations on my results. The structure of the
NLSY, which is a cohort sample of persons who aged together over time and experienced
similar macroeconomic trends, limits the generalizability of my findings. While I expect
previously incarcerated persons of any cohort to accumulate limited wealth, the disparities
will vary based upon the broader economic and political situation. In particular, my
analysis ends before the recent recession that likely exacerbated group disparities in
wealth.
Due to the available measures of incarceration, these findings provide limited, and
likely conservative, estimates of the effects of incarceration on home ownership and wealth
accumulation. My measure underestimates the rate of incarceration for this sample because
J Quant Criminol (2015) 31:207–236 231
123
it captures only whether the respondent was incarcerated at the time of the survey inter-
view. Many respondents may have also ‘‘aged-out’’ of the key at-risk population for
offending prior to the years of increasing mass incarceration in the United States. Addi-
tionally, the data do not include the reason for incarceration, which may affect incarcer-
ation’s relationship with home ownership and net worth. Thus, my results might not
generalize across all types of criminal activity.
Conclusion
My results show that incarceration directly and indirectly disadvantages previously
incarcerated persons in terms of wealth accumulation. Compared to never-incarcerated
persons, ex-offenders are less likely to own their homes by an average of 5 percentage
points, and specifically after incarceration, when compared to their average probability of
home ownership prior to incarceration, the probability decreases by 28 percentage points.
Former prisoners have a lower net worth, which also decreases by an average of $42,000 in
the years after incarceration. Thus, my research shows that, in terms of home ownership
and net worth, previously incarcerated persons begin on worse trajectories and experience
added negative effects on these outcomes after being incarcerated.
Outside of these substantive contributions, this paper offers a theoretical contribution by
bringing together ascription, stigma, and cumulative advantage in order to show how
incarceration acts as an overarching absorbing status. I use the term ‘‘absorbing status’’ to
indicate first, how an achieved status, such as incarceration, can act as if it were ascribed, and
second, how ascription is linked with the accumulation of disadvantage. Once achieved, a
previous incarceration becomes a stigmatized mark of dishonor that brands a person as
untrustworthy. When stigmatized and credentialized, incarceration status can have far-
reaching, cumulative effects. The extension of its negative effects to multiple areas, in which
it may not be a useful predictor of performance or trustworthiness, indicates that once
achieved, this status in many ways acts as if it were ascribed. Through this process incar-
ceration can feed into a system that continually disadvantages members of certain groups.
My findings are consistent with a model of accumulating disadvantage, where certain
statuses lead to the accumulation of disparities. In particular, the differences in net worth
depend on the previously incarcerated person’s time and length of incarceration. As shown
by models that included these measures, the effects of incarceration on net worth were
more pronounced later on and with longer periods of incarceration. The effects of incar-
ceration on wealth accumulation were incredibly long lasting. Due to the nature of the
NLSY data, respondents who were incarcerated longer ago were also incarcerated at a
younger age. Thus, there has been more time for the status of incarceration to affect
multiple areas of their lives.
My research shows that disadvantage can also be compounded when the negative
effects of different statuses build upon each other. Enduring wealth barriers already impede
the social and economic mobility of individuals from disadvantaged race and class groups.
Income and education are associated with wealth (Dynan and Kohn 2007; Keister 2000a).
Black/white racial wealth disparities that exceed income disparities continue today as
white families pass on more wealth to their offspring than black families (Conley 1999;
Oliver and Shapiro 1997; Shapiro 2004). Significant disparities in wealth accumulation,
home ownership rates, and home equity also exist between white and Hispanic households
(Flippin 2001; Krivo and Kaufman 2004; Campbell and Kaufman 2006). Having been
incarcerated adds to these inequalities.
232 J Quant Criminol (2015) 31:207–236
123
In my models, earnings and race were consistently associated with home ownership and
net worth; when combined with incarceration status, low-earnings and racial minority
status added to this disadvantage. My mediation models also demonstrated how incar-
ceration indirectly affected wealth accumulation through its negative effects on other areas,
including the labor market. The additive negative consequences of disadvantaged statuses
on wealth outcomes are obvious in this paper. Disadvantage then not only accumulates
over time for previously incarcerated people, but incarceration also adds to the obstacles
already in place for some of the least advantaged people.
With the multiple mechanisms behind my findings, this research provides further evi-
dence that incarceration is an absorbing status by demonstrating the continuing disad-
vantage that ex-convicts face after they complete their prison sentences. Incarceration is
associated with diminished education and employment opportunities, stagnant earnings,
health problems, and family breakup for many former offenders (Wakefield and Uggen
2010). I add home ownership and net worth to this growing list. My findings, when taken in
conjunction with those focused on the effects of incarceration on labor market, health,
family, and political situations, portray a criminal justice system that places additional
burdens on some of the more disadvantaged and less powerful members of society in
conjunction with incarceration’s absorbing status.
Acknowledgments I gratefully acknowledge Barbara Reskin for her insightful comments on drafts of this
paper. I would also like to thank Becky Pettit and the participants of the University of Washington Sociology
Deviance Seminar for their feedback. Partial support for this research came from a Eunice Kennedy Shriver
National Institute of Child Health and Human Development research infrastructure Grant, R24 HD042828,
to the Center for Studies in Demography & Ecology at the University of Washington.
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... We are aware of only two studies that specifically examined the effects of arrests on young adult asset and debt attainment. First, Maroto (2015) used regression analyses and portions of the data that we use here to assess whether having ever been arrested by age 25 predicted various measures of age 25 wealth accumulation. She found that a previous arrest decreased financial assets by 45% and debt by 35%, though it did not decrease total net worth. ...
... Still, several questions remain unanswered. First, Maroto (2015) examined arrest histories up to age 25, so her findings may speak more to the effects of "youthful" arrests-those occurring up to early young adulthood-than to the lasting effects of juvenile arrests specifically. And Maroto and Sykes' (2019) findings speak to adult arrests. ...
... Thus, we do not yet know the implications of juvenile arrests for these outcomes. Second, Maroto (2015) was unable to conduct causal analyses of the impact of arrests on asset and debt accumulation, so it is possible that her findings overstated the presence or size of that impact. And third, the youthful arrest analyses of Maroto's (2015) study did not include mediation analyses, so they did not reveal why any association between youthful arrests and later economic attainment might occur. ...
Article
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Objectives We tested the impact of juvenile arrest on asset accumulation, debt accumulation, and net worth from ages 20–30. We also examined whether indicators of family formation, school and work attainment, and subsequent justice system contacts explained any effects. Methods We used longitudinal data on 7916 respondents from the National Longitudinal Survey of Youth 1997 Cohort. Our treatment variable was a dichotomous indicator of whether respondents were arrested as juveniles. Our focal outcomes were combined measures of the values of 10 types of assets, 6 types of debt, and net worth (assets minus debt) at ages 20, 25, and 30. We used propensity score methods to create matched groups of respondents who were and were not arrested as juveniles, and we compared these groups on the outcomes using multilevel growth curve analyses. Results Arrested juveniles went on to have lower assets, debts, and net worth during young adulthood compared to non-arrested juveniles. These differences were most pronounced at age 30. The differences were largely explained by educational attainment, weeks worked, and income. Conclusions The fact that juvenile arrest predicted early adult economic attainment net of 43 matching covariates provides strong evidence that these effects are not merely artifacts of selection. The additional finding that education, employment, and income explain much of the juvenile arrest effect highlights several potential areas of intervention for protecting young arrestees’ later net worth.
... First, using data from the 1997 cohort of the National Longitudinal Survey of Youth (NLSY97), we ask whether criminal justice contact is linked to access to credit and indebtedness-an important but understudied aspect of socioeconomic inequality in the twenty-first century (Dwyer 2018). Criminal justice contact, particularly incarceration, contributes to labor market inequalities (Wakefield and Uggen 2010), and scholars have recently turned their attention to outcomes such as net worth, assets, and debts (Dwyer, DeMarco, and Haynie 2019;Maroto 2015;Maroto and Sykes 2019;Sykes and Maroto 2016;Turney and Schneider 2016;Zaw, Hamilton, and Darity 2016). We expand this research by examining the relationship between criminal justice contact and unsecured debt that is accrued from credit cards, banks, and other businesses. ...
... More recently, scholars have examined how criminal justice contact is implicated in wealth inequality in young adulthood, including outcomes such as net worth, homeownership, and owning a bank account (Maroto 2015;Maroto and Sykes 2019;Remster and Kramer 2018;Schneider and Turney 2015;Sykes and Maroto 2016;Turney and Schneider 2016). Young adulthood is a critical time period for socioeconomic attainment and mobility (Furstenberg 2008) when debt accumulates rapidly (Houle 2014a;Yilmazer and Devaney 2005). ...
... Finally, criminal justice contact could prohibit access to credit because any resulting discrimination and socioeconomic disadvantage could damage an individual's credit standing. Contact at all levels is detrimental to employment prospects (Pager 2007;Uggen et al. 2014), and declines in net worth and homeownership (Maroto 2015;Turney and Schneider 2016) could impede an individual's ability to develop a credit history. ...
Article
Contact with the American criminal justice system is associated with socioeconomic disadvantage and financial insecurity, but little research has explored the link between criminal justice contact and indebtedness. In this study, we ask whether contact in young adulthood is associated with access to credit and unsecured debt burdens. We also focus on state-level policies that operate alongside official punishments and restrict citizenship and societal participation among the justice-involved (termed hidden sentences), and ask whether such policies moderate the association between criminal justice contact and indebtedness. We find that criminal justice contact, especially incarceration, is associated with reduced access to unsecured credit and greater absolute and relative debt burdens. These associations are strongest for individuals residing in states with more onerous hidden sentence regimes. We argue that indebtedness is a key socioeconomic consequence of criminal justice contact and that hidden sentences may exacerbate these consequences.
... Recently, researchers have also begun to focus on incarceration's consequences for wealth, an important but often overlooked dimension of economic inequality and financial insecurity. Studies using different datasets indicate that incarceration leads to declines in homeownership and net worth (Maroto 2015;Zaw, Hamilton, and Darity 2016), limits wealth for other household members (Sykes and Maroto 2016), and impedes ownership of bank accounts and vehicles (Turney and Schneider 2016). Using data from the 1997 cohort of the National Longitudinal Survey of Youth (NLSY97), we expand on previous work, which has largely focused solely on incarceration among baby boomers, by investigating how different types of criminal justice contact influence multiple components of wealth among this younger cohort. ...
... It limits access to and advancements in education, employment, and political spheres Wakefield and Uggen 2010) and creates short-and long-term health problems for former prisoners (Massoglia 2008a(Massoglia , 2008bSykes and Piquero 2009), which may compound existing economic inequalities. For people with criminal records, incarceration operates as a criminal credential (Pager 2007) or an absorbing status (Maroto 2015), defined as an indefinite and perpetual state of disadvantage that compounds and solidifies hardships and inequalities, from which upward mobility is unlikely the longer the person remains in this steady-state. The absorbing status of incarceration is instrumental in fostering social exclusion (Foster and Hagan 2015). ...
... Previous research indicates that the negative consequences of incarceration also extend to wealth among older adults, particularly those within the baby boomer cohort. Maroto (2015) found that, in addition to starting with lower levels of wealth, the likelihood of homeownership declined by an additional 28 percentage points and an ex-offender's net worth decreased by an average of $42,000 after incarceration among baby boomers in NLSY79 data. A further investigation of the indirect effects of incarceration showed that incarceration coincided with income losses, which also limited ex-offenders' abilities to build wealth (Maroto 2015). ...
Article
Previous research indicates that incarceration leads to declines in rates of homeownership and net worth, especially among baby boomers, but questions remain as to how other types of criminal justice system contact affect wealth outcomes during the transition to adulthood. Using data from the 1997 National Longitudinal Survey of Youth, we investigate how arrests, convictions, and incarceration influence net worth, financial assets, and debt among young adults. We find that most contact with the criminal justice system limited the ability of young adults to accumulate wealth between the ages of 25 and 30, an especially important time for building life-cycle wealth. Arrests were associated with asset and debt declines of 52–53 percent, and incarceration led to net worth and asset declines of 34 and 76 percent, respectively. These direct effects were also bolstered by the indirect effects of these variables through their relationship with marriage and earnings, especially in the case of incarceration. This study draws attention to how criminal justice system contact affects early adult wealth, thereby setting the stage to influence a host of life course dynamics for individuals and their families.
... These data often consist of large nationally representative samples, are longitudinal in nature, and provide multiple measures of employment outcomes. Research using survey data has consistently found that incarcerated individuals have worse employment prospects than non-incarcerated individuals (Apel & Sweeten, 2010;Geller, Garfinkel, & Western, 2006;Jung, 2015;Maroto, 2015;Western, 2002;Zaw, Hamilton, & Darity, 2016). Across the existing research, estimates indicate that incarcerated individuals' probability of employment is about 10-20% lower than non-incarcerated individuals and their wage earnings are around 5-30% lower (Apel & Sweeten, 2010;Geller et al., 2006;Jung, 2015;Maroto, 2015;Western, 2002;Zaw et al., 2016). ...
... Research using survey data has consistently found that incarcerated individuals have worse employment prospects than non-incarcerated individuals (Apel & Sweeten, 2010;Geller, Garfinkel, & Western, 2006;Jung, 2015;Maroto, 2015;Western, 2002;Zaw, Hamilton, & Darity, 2016). Across the existing research, estimates indicate that incarcerated individuals' probability of employment is about 10-20% lower than non-incarcerated individuals and their wage earnings are around 5-30% lower (Apel & Sweeten, 2010;Geller et al., 2006;Jung, 2015;Maroto, 2015;Western, 2002;Zaw et al., 2016). ...
Article
Objectives Although reentry is commonly perceived as a singular event, evidence suggests that when formerly incarcerated individuals reenter society they have a heightened likelihood of returning to prison. This heightened likelihood could generate a reentry-reincarceration cycle, where individuals reenter society, become reincarcerated due to situational circumstances, and have to go through reentry again. This cycle is likely to continue until the barriers to reentry are addressed. One known barrier to reentry is the inability to gain and maintain legal employment. While research suggests that incarceration diminishes future employment opportunities, scholars have yet to evaluate the between-individual and within-individual effects of the reentry-reincarceration cycle on future employment outcomes. Methods Using the NLSY97 birth cohort, the current study evaluated the influence of time spent incarcerated (an approximation of the reentry-reincarceration cycle) on future employment outcomes over an 18-year period. Specifically, two cross-lagged panel models were estimated to examine the between-individual effects of the number of months incarcerated on employment and the number of weeks employed, while two lagged latent growth models were estimated to examine the within-individual effects. Results In addition to suggesting that the reentry-reincarceration cycle exists, the findings illustrated that the reentry-reincarceration cycle influences between-individual differences on employment outcomes and within-individual changes in employment outcomes over time. Conclusion The reentry-reincarceration cycle contributes to the difficulties formerly incarcerated individuals have finding and maintaining employment during reentry. Efforts should be made to diminish the likelihood of individuals falling into the reentry-reincarceration cycle, as well as increase employment opportunities for formerly incarcerated individuals.
... Although many scholars have discussed the regressive interaction of monetary sanctions and public assistance, the motivation of this article is to move the conversation away from recognizing monetary sanctions as a collateral consequence of the penal system, and toward the rec-ognition that, when financial sanctions are applied to the poor, particularly those who are the recipients of public assistance, these sanctions act as an exponent of punishment by depriving individuals and communities of social safety net provisions (see also Harris 2016). Recent scholarship has helped clarify the struggles of reentry (Western 2018), echoing qualitative (Macleod 2009) and quantitative (Pager 2003;Pager and Lincoln 2005;Petitt and Lyons 2009) findings that a criminal record is an "absorbing status" (Maroto 2015) that profoundly reduces labor access and wealth generation (Sykes and Maroto 2016;Maroto and Sykes 2020). Our work contributes to scholarship on the feedback effects of poverty and LFOs by specifying how money is, often unwittingly, transferred from assistance programs to penal institutions in the imposition of monetary sanctions. ...
Article
Research on punishment and inequality finds that people with criminal records routinely avoid systems of surveillance. Yet scholarship on monetary sanctions shows that many people experiencing poverty with criminal legal system debt are also involved with the state in other domains of social life. How can these literatures be resolved? In this article, we posit that past research can be reconciled through a focus on financial double-dealing—disparate and contradictory economic entanglements that redistribute welfare resources from individuals to the criminal legal system and its institutional affiliates. Drawing on nationally representative survey data, as well as unique data collected on people with monetary sanctions in seven states, we find that individuals and families receiving cash and noncash public assistance are significantly more likely to owe monetary sanctions and are less likely to pay them. We discuss the implications of multiple-system involvement for ongoing surveillance.
Article
Prior consumer research has tended to focus on identity-related stigma management of individuals toward their own stigma. However, little is known about the consumption-related identity work that stigmatized individuals undertake to discharge the courtesy stigma attached to close associates such as family members. Courtesy stigma refers to the discredit directed toward people who are closely associated with a stigmatized individual or group. Drawing on interview, ethnographic, and netnographic data on China’s ‘Leftover Women,’ our research analyzes the personal and, more centrally, the social identity work related consumption counternarratives that these women construct—through combinations of specific kinds of consumption and gift-giving practices—to counteract family and courtesy stigma. Counternarratives are the resistance stories that people tell and live to either implicitly or explicitly challenge the dominant cultural narrative. The findings of our investigation help to build an enhanced understanding of how stigmatized individuals act as consumers in the market and via digital channels to tackle the family identity challenges of courtesy stigma that have not been explored in extant studies of consumer stigma identity work.
Article
Rates of incarceration in the United States have grown dramatically over the past 50 years. These high rates of incarceration call for mental health and relational therapy to incarcerated individuals and their families. In conducting a literature review on incarceration, several topics emerged: mental illness, racial and ethnic disparity, and recidivism. When studying incarceration, mental illness is a necessary topic of inclusion due to high prevalence of mentally ill incarcerated individuals. When exploring issues related to incarceration, it is important to discuss diverse disparities to be able to put the individuals into context of their social location as well as address how contextual factors impact incarceration. The purpose of this article is to highlight the systemic, relational issues within incarcerated settings and then to display how treating mental illness and relational concerns allows for a healthier integration back into the family system. Clinical implications and future directions are also provided.
Article
Social exclusion of those with criminal justice experience increasingly includes a financial component, but the structure of disadvantage in credit and debt remains unclear. We develop a model of financial disadvantage in debt holding during the transition to adulthood among justice-involved groups. We study cumulative criminal justice contact and debt holding by age 30 using the National Longitudinal Survey of Youth 1997 (NLSY97). The NLSY97 cohort transitioned to adulthood during an era of historically high criminal justice contact, with many experiencing arrests, convictions, and incarceration. We develop a distinct measurement approach to cumulative criminal justice contact by age 30 that captures variation between young adults in the severity of justice encounters in the early life course. We conceptualize financial disadvantage as a lower likelihood of holding debt that facilitates property and attainment investments and a higher likelihood of holding higher cost debts used for consumption or emergencies. We find that those with the most punitive criminal justice contact evidence the most disadvantageous form of debt holding, potentially exacerbating social exclusion. We consider the implications of the accumulation of financial disadvantage for our understanding of criminal justice contact as a life-course process.
Article
Objective The authors investigate whether criminal justice contact is associated with residential transitions—both home‐leaving and home‐returning—among contemporary young adults. Background More young adults live with their parents today than live independently. Despite the prevalence of criminal justice contact among young Americans, and research suggesting that such contact can reshape the life course, it is unknown whether the criminal justice system is associated with patterns of home‐leaving and home‐returning. Method Data are drawn from the National Longitudinal Survey of Youth 1997, a sample of 8,984 young adults born between 1980 and 1984. Event history analyses are used to examine the timing of home‐leaving (n = 810,274 person‐months), and among those who leave, the timing of home‐returning (n = 630,394 person‐months). Criminal justice contact is measured via self‐reported arrests and spells of incarceration. Results Across both the short term and long term, there is a robust association between criminal justice contact and residential transitions out of and back into the parental home. The risk of experiencing home‐leaving or home‐returning is considerably higher in the month an individual is arrested or completes a spell of incarceration, compared to individuals with no contact. Additionally, especially for arrest, the risk of each residential transition remains elevated in the months and years that follow contact. Conclusion Findings highlight how the criminal justice system may complicate the transition to adulthood by contributing to unstable residential trajectories.
Article
This article extends the existing literature about the consequences that having a prison record has on formerly incarcerated men’s labor market outcomes by projecting forward to think about what the diminished labor market prospects may mean for men when they reach retirement age. We find that formerly incarcerated men have little wealth accumulated by their late 40s and 50s, that they have limited access to on-the-job pensions, and that some may not even be able to rely on Social Security when they are old. The article uses data from the National Longitudinal Survey of Youth (NLSY79) and focuses most of its attention on the plight of Black men and Latino men, as this is the subset of the population that was particularly affected by the nation’s mass incarceration policies of the late 20th century. The implications of the findings for Black and Brown men’s prospects during old age are discussed, as are the implications for the way that policy scholars think about race, aging, and public policy.
Article
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The concept of vacancy chains, originally developed in Harrison White’s pioneering analysis of organizational mobility processes, has been extended to phenomena as diverse as national labor and housing markets, the historical development of professions, gender and ethnic group discrimination in job and housing markets, organizational demography, and the mobility of hermit crabs to empty snail shells. In all populations in which they occur—whether human or animal—vacancy chains appear to organize a variety of social processes in nearly identical ways. This chapter provides a broad and relatively nonmathematical review of the vacancy chain literature covering basic definitions and formulations, main theoretical ideas and assumptions, comparisons of social processes in different vacancy chain systems, and several conceptual and methodological extensions to vacancy chain analysis. The review concludes by discussing a number of outstanding problems, present limitations, and promising areas for future research using the vacancy chain approach to mobility.
Code
R package for Data Analysis using multilevel/hierarchical model
Article
Testing multilevel mediation using hierarchical linear modeling (HLM) has gained tremendous popularity in recently years. However, biases could arise when no centering or grand-mean centering is used in these models. This study first summarizes three types of HLM-based multilevel mediation models, and then explains that in two types of these models, biases are produced when using current procedures of testing multilevel mediation. A Monte Carlo study was conducted to illustrate that HLM applied to grand-mean-centered data can under- or overestimate true mediational effects. Recommendations are provided with regard to the differentiation of within-group versus between-group mediation in multilevel settings.
Article
Prison growth over the last quarter of the twentieth century is notable not only for its magnitude but also for the fact that it has disproportionately affected already disadvantaged segments of the population. The prison buildup generates three important observations about inequalities related to incarceration. African Americans are seven times more likely than whites to serve time in prison; educational disproportionality in exposure to the criminal-justice system has increased; and nonviolent offenders represent a growing proportion of prison inmates (Pettit and Western 2004; Mauer 1999). Criminal offenders and prison inmates face poor labor-market opportunities. Employers express a reluctance to hire ex-inmates (Pager 2003; Holzer 1996), and ex-inmates face earnings penalties of between 10 and 30 percent (Western, Kling, and Weiman 2001). Research that finds persistent incarceration penalties, even when controlling for indicators of productivity and criminal embeddedness, suggests the causal relevance of the stigma of incarceration. Explanations for incarceration effects that emphasize the stigma of contact with the criminal-justice system claim that individuals are typed or labeled as "essentially deviant" by formal agents of the criminal-justice system. Exposure to the criminal-justice system precedes the existence of any secondary or more positive characteristics of an individual, and the general consequence of being labeled essentially deviant means that one is viewed as less trustworthy and rule-abiding (Becker 1963). Although there is clear evidence of disproportionality in exposure to the criminal-justice system, we know relatively little about how the effects of incarceration vary by race, class, or criminal involvement. The ubiquity of incarceration among low-skilled minority men leads us to reconsider the relevance of social stigma for the economic fortunes of inmates. In an environment of "mass incarceration" (Garland 2001), stigma effects may be less severe, as incarceration may not provide a clear signal of trustworthiness or future productivity to employers. Harry J. Holzer, Steven Raphael, and Michael Stoll (2003) argue that the effects of incarceration may be minimized because employers statistically discriminate against groups with high rates of incarceration (for example, young black low-skilled men). Similarly situated young men already face poor job prospects due to structural divisions within labor markets that relegate low-skilled workers to jobs with low wages and little chance for earnings growth (Bernhardt et al. 2001). This chapter examines how the effects of incarceration on post-release employment and wages depend on offender characteristics. In order to investigate this question we assembled an administrative data set containing information on individuals who were in a Washington State prison between 1990 and 2000, linked to demographic, education, and earnings records. We use these data to examine post-release employment and wages in jobs covered by unemployment insurance (UI). We investigate how the effects of spending time in prison vary by race, education, and a general measure of criminal severity. We find that incarceration has mixed effects on post-release employment. Offenders experience temporary gains in UI-covered employment in the first year after release from prison, although the effect declines with time out of prison. Incarceration is consistently associated with declines in post-release wages. Subgroup differences in incarceration effects emphasize the significance of social stigma explanations, particularly for high-status offenders. The negative effects of incarceration on wages are greatest for those who are least likely to spend time in prison and for whom spending time in prison is strongly counternormative. Racial differences in post-release employment and wages highlight race as a "master status" and suggest that incarceration effects must be considered in relation to growing inequality in labor-market outcomes generated by structural divisions in labor markets. Prison incarceration has become a common occurrence among young low-skilled men in recent cohorts, and the penal system has taken on growing significance in accounts of inequality. Men at high risk of incarceration (for example, low-skilled, black) are not more heavily penalized for spending time in prison than other offenders. However, all ex-inmates experience post-release declines in wages, and declines are enduring for blacks. Further, even small wage penalties, when considered in relation to high rates of incarceration among disadvantaged men, deepen inequality and reinforce the growing cleavage between the economic opportunities of men involved in the criminal-justice system and those outside of it.
Article
The award-winning Black Wealth / White Wealth offers a powerful portrait of racial inequality based on an analysis of private wealth. Melvin Oliver and Thomas Shapiro's groundbreaking research analyzes wealth - total assets and debts rather than income alone - to uncover deep and persistent racial inequality in America, and they show how public policies have failed to redress the problem.