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.
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.
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.
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.
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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.
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.
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.
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.
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.
I use a logit form of this equation to estimate an individual’s probability of home ownership at time, t.
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).
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 significant and negative.
I removed cases with a total net worth greater than $2 million or less than −$2 million 2010 USD. This removed 313 cases.
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.
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 incarcerated 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.
Age also acts as a proxy for time or year because the NLSY is a longitudinal cohort sample.
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.
To simplify this set of analyses, I coded marital status as currently married or not currently married for the mediation models.
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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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Maroto, M.L. The Absorbing Status of Incarceration and its Relationship with Wealth Accumulation. J Quant Criminol 31, 207–236 (2015). https://doi.org/10.1007/s10940-014-9231-8