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.
This is a preview of subscription content, access via your institution.
Buy single article
Instant access to the full article PDF.
Tax calculation will be finalised during checkout.
Subscribe to journal
Immediate online access to all issues from 2019. Subscription will auto renew annually.
Tax calculation will be finalised during checkout.
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.
Allison PD (2009) Fixed effects regression models. Sage Publications, California
Apel R, Sweeten G (2010) The impact of incarceration on employment during the transition to adulthood. Soc Probl 57(3):448–479
Apel R, Blokland AAJ, Niewbeerta P, van Schellen M (2010) The impact of imprisonment on marriage and divorce: a risk set matching approach. J Quant Criminol 26:269–300
Bauer DJ, Preacher KJ, Gil KM (2006) Conceptualizing and testing random indirect effects and moderated mediation in multilevel models: new procedures and recommendations. Psychol Methods 11(2):142–163
Bjerk D (2009) How much can we trust causal interpretations of fixed-effects estimators in the context of criminality? J Quant Criminol 25(4):391–417
Breen R, Karlson KB, Holm A (2013) Total, direct, and indirect effects in logit models. Sociol Methods Res 42(2):164–191
Bricker J, Kennickell A, Moore K, Sabelhaus J (2012) Changes in U.S. family finances from 2007 to 2010: evidence from the survey of consumer finances. Fed Reserve Bull 98:1–80
Bucks BK (2012) Out of balance? Financial distress in U.S. households. In Porter K (Ed.) Broke: how debt bankrupts the middle class. Stanford University Press, Stanford
Bureau of Justice Statistics (BJS). (2010) Prison inmates at midyear 2009-statistical tables, NCJ 230113. Washington, DC: U.S. GPO, U.S. Department of Justice. Retrieved on August 5, 2011. (http://www.bjs.ojp.usdoj.gov/index.cfm?ty=pbdetail&iid=2200)
Cadge W, Davidman L (2006) Ascription, choose, and the construction of religious identities in the contemporary United States. J Sci Study Relig 45(1):23–38
Campbell LA, Kaufman RL (2006) Racial differences in household wealth: beyond black and white. Res Soc Stratif Mobil 24:131–152
Carson EA, Sabol WJ (2012) Prisoners in 2011. U.S. Department of Justice, Office of Justice Programs, Bureau of Justice Statistics. NCJ 2399808
Chase ID (1991) Vacancy chains. Ann Rev Sociol 17:133–154
Conley D (1999) Being black, living in the red: race, wealth, and social policy in America. University of California Press, California
Curran PJ, Bauer DJ (2011) The disaggregation of within-person and between-person effects in longitudinal models of change. Annu Rev Psychol 62:583–619
DiPrete TA, Eirich GM (2006) Cumulative advantage as a mechanism for inequality: a review of theoretical developments. Annu Rev Sociol 32:271–297
Dynan KE, Kohn DL (2007) The rise in U.S. household indebtedness: Causes and consequences. Finance and Economics Discussion Series Working Paper No. 2007-37
Farkas G (2003) Cognitive skills and noncognitive traits and behaviors in stratification processes. Annu Rev Sociol 29:541–562
Flippin CA (2001) Racial and ethnic inequality in home ownership and housing equity. Sociol Q 42(2):121–149
Freeman RB (1992) Crime and the employment of disadvantaged youth. In: Peterson G, Vroman W (eds) Urban labor markets and job opportunity. Urban Institute, Washington, DC, pp 201–237
Freeman RB (1996) Why do so many young American men commit crimes and what might we do about it? J Econ Perspect 10(1):25–42
Gelman A, Hill J (2007) Data analysis using regression and multilevel/hierarchical models. Cambridge University Press, New York
Goffman E (1963) Stigma: notes on the management of spoiled identity. Simon & Schuster, New York
Gottfredson MR, Hirschi T (1990) A general theory of crime. California University Press, California
Harris A, Evans H, Beckett K (2010) Drawing blood from stones: legal debt and social inequality in the contemporary United States. Am J Sociol 115(6):1753–1799
Harris A, Evans H, Beckett K (2011) Courtesy stigma and monetary sanctions: toward a socio-cultural theory of punishment. Am Sociol Rev 76(2):234–264
Henry NW, McGinnis R, Tegtmeyer HW (1971) A finite model of mobility. J Math Sociol 1:107–118
Holzer HJ (1996) What do employers want? Job prospects for less-educated workers. Russell Sage, New York
Holzer HJ, Raphael S, Stoll MA (2003) Employment barriers facing ex-offenders. Paper presented at Urban Institute Reentry Roundtable, employment dimensions of reentry: understanding the nexus between prisoner reentry and work, May 19–20, 2003, New York University Law School
Jacobsen B, Kendrick JM (1973) Education and mobility: from achievement to ascription. Am Sociol Rev 38:439–460
Karlson KB, Holm A (2011) Decomposing primary and secondary effects: a new decomposition method. Res Stratif Soc Mobil 29:221–237
Karlson KB, Holm A, Breen R (2011) Comparing regression coefficients between same-sample nested models using logit and probit: a new method. Sociol Methodol 42:286–313
Keister LA (2000a) Wealth in America: trends in wealth inequality. Cambridge University Press, New York
Keister LA (2000b) Race and wealth inequality: the impact of racial differences in asset ownership on the distribution of household wealth. Soc Sci Res 29:477–502
Keister LA, Moller S (2000) Wealth inequality in the United States. Annu Rev Sociol 26:63–81
Kemper TD (1974) On the nature and purpose of ascription. Am Sociol Rev 39(6):844–853
Kenworthy L (2007) Inequality and sociology. Am Behav Sci 50:584–602
Kling JR (2006) Incarceration length, employment, and earnings. Am Econ Rev 96(3):863–876
Krivo LJ, Kaufman RL (2004) Housing and wealth inequality: racial–ethnic differences in home equity in the United States. Demography 41(3):585–605
Krull JL, MacKinnon DP (2001) Multilevel modeling of individual and group level mediated effects. Multivar Behav Res 36(2):249–277
Link BG, Phelan JC (2001) Conceptualizing stigma. Annu Rev Sociol 27:363–385
Lopoo LM, Western B (2005) Incarceration and the formation and stability of marital unions. J Marriage Family 67(3):721–734
Lorber J (1994) The paradoxes of gender. Yale University Press, Connecticut
Lott JR Jr (1990) The effect of conviction on the legitimate income of criminals. Econ Lett 34:381–385
Lyons CJ, Pettit B (2011) Compounded disadvantage: race, incarceration, and wage growth. Soc Probl 58(2):257–280
MacKinnon DP, Dwyer JH (1993) Estimating effects in prevention studies. Eval Rev 17(2):144–158
Manza J, Uggen C (2006) Locked out: felon disenfranchisement and American democracy. Oxford University Press, New York
Massoglia M (2008a) Incarceration, health, and racial disparities in health. Law Soc Rev 42(2):275–306
Massoglia M (2008b) Incarceration as exposure: the prison, infectious disease, and other stress-related illnesses. J Health Soc Behav 49(1):56–71
Massoglia M, Firebaugh G, Warner C (2013) Racial variation in the effect of incarceration on neighborhood attainment. Am Sociol Rev 78(1):142–165
Matras J (1967) Social mobility and social structure: some insights from the linear model. Am Sociol Rev 32(4):608–614
Maume DJ Jr, Cancio AS, Evans TD (1996) Cognitive skills and racial wage inequality: reply to Farkas and Vicknair. Am Sociol Rev 61(4):561–564
Mayhew L (1968) Ascription in modern societies. Sociol Inq 38(2):105–120
McGinnis R (1968) A stochastic model of social mobility. Am Sociol Rev 33:712–722
Merton RK (1968) The Matthew effect in science. Science 159(3810):56–63
Merton RK (1988) The Matthew effect in science, II: cumulative advantage and the symbolism of intellectual property. ISIS 79:606–623
Modigliani F (1986) Life cycle, individual thrift, and the wealth of nations. Am Econ Rev 76:297–313
Nagin D, Waldfogel J (1998) The effect of conviction on income through the life cycle. Int Rev Law Econ 18:25–40
NLSY79 user’s guide: a guide to the 1979–2006 National Survey of Youth Data. Prepared for the U.S. Department of Labor by Center for Human Resource Research. Ohio State University, Center for Human Resource Research, Columbus, Ohio
Oliver M, Shapiro T (1997) Black wealth/white wealth: a new perspective of racial inequality. Routledge, New York
Pager D (2003) The mark of a criminal record. Am J Sociol 108:937–975
Pager D (2007) Marked: race, crime, and finding work in an era of mass incarceration. University of Chicago Press, Illinois
Pager D, Quillian L (2005) Walking the talk: what employers say versus what they do. Am Sociol Rev 70:355–380
Pager D, Bonikowski B, Western B (2009) Discrimination in a low-wage labor market: a field experiment. Am Sociol Rev 79:777–799
Pettit B, Lyons C (2007) Status and the stigma of incarceration: the labor market effects of incarceration by race, class, and criminal involvement. In Bushway S, Stoll MA, Weiman DF (eds.) Barriers to reentry? The labor market for released prisoners in post-industrial America. Russell Sage Foundation, New York, pp. 203–226
Pettit B, Western B (2004) Mass imprisonment and the life course: race and class inequality in U.S. incarceration. Am Sociol Rev 69(2):151–169
Piore MJ (1970) The dual labor market: theory and implications. In Beer SH, Barringer RE (eds) The state and the poor, Winthrop, pp. 55–59
Reskin BF (2012) The race discrimination system. Annu Rev Sociol 38:17–35
Sampson RJ, Laub JH (1997) A life-course theory of cumulative disadvantage and the stability of delinquency. In: Thornberry TP (ed) Developmental theories of crime and delinquency: advances in criminological theory. Transaction Publishers, New Jersey
Samuels P, Mukamel D (2004) After prison—roadblocks to reentry: a report on state legal barriers facing people with criminal records. Legal Action Center, New York
Schnittker J, John A (2007) Enduring stigma: the long-term effects of incarceration on health. J Health Soc Behav 48(2):115–130
Schnittker J, Massoglia M, Uggen C (2011) Incarceration and the health of the African American community. Du Bois Review 8(1):133–141
Shapiro TM (2004) The hidden cost of being African American: how wealth perpetuates inequality. Oxford University Press, New York
Smith JP (1999) Healthy bodies and thick wallets: the dual relation between health and economic status. J Econ Perspect 13(2):145–166
Sobel ME (1982) Asymptotic confidence intervals for indirect effects in structural models. Sociol Methodol 13:290–312
Spilerman S (2000) Wealth and stratification processes. Annu Rev Sociol 26:497–524
Turney K, Schneider D (2014) Incarceration and household wealth. Paper presented at the 2014 PAA Conference, Boston, MA
Turney K, Wildeman C (2013) Redefining relationships: explaining the countervailing consequences of paternal incarceration for parenting. Am Sociol Rev 78:949–979
U.S. Department of Defense (1982) Profile of American Youth: 1980 nationwide administration of the armed services vocational aptitude battery. Office of the Assistant Secretary of Defense, Washington, DC
Wacquant L (2001) Deadly symbiosis: when ghetto and prison meet the mesh. Punishm Soc 3(1):95–134
Wakefield S, Uggen C (2010) Incarceration and stratification. Annu Rev Sociol 36:387–406
Waldfogel J (1994) The effect of a criminal conviction on income and the trust ‘reposed in the workmen’. J Human Resour 29(1):62–81
Western B (2002) The impact of incarceration on wage mobility and inequality. Am Sociol Rev 67(4):526–546
Western B (2006) Punishment and inequality in America. Russell Sage, New York
Western B, Beckett K (1999) How unregulated is the U.S. labor market? The penal system as a labor market institution. Am J Sociol 104:1030–1060
Western B, Pettit B (2000) Incarceration and racial inequality in men’s employment. Ind Labor Relat Rev 54(1):3–16
Western B, Kling JR, Weiman DF (2001) The labor market consequences of incarceration. Crime Delinq 47:410–427
White H (1970) Chains of opportunity. Harvard Press, Massachusetts
Wildeman C (2009) Parental imprisonment, the prison boom, and the concentration of childhood disadvantage. Demography 46(2):265–280
Wildeman C (2010) Paternal incarceration and children’s physically aggressive behaviors: evidence from the fragile families and child wellbeing study. Soc Forces 89(1):285–310
Wildeman C, Muller C (2012) Mass imprisonment and inequality in health and family life. Annu Rev Law Soc Sci 8:11–30
Zhang Z, Zyphur MJ, Preacher KJ (2009) Testing multilevel mediation using hierarchical linear models problems and solutions. Organ Res Methods 12(4):695–719
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.
Electronic supplementary material
Below is the link to the electronic supplementary material.
About this article
Cite this article
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
- Cumulative disadvantage
- Home ownership