Abstract
Objectives
I examine housing instability among individuals with a felony conviction but no incarceration history relative to formerly incarcerated individuals as a means of separating the effect of felon status from that of incarceration per se—a distinction often neglected in prior research. I consider mechanisms and whether this relationship varies based on gender, race/ethnicity, time since conviction, and type of offense.
Methods
I use National Longitudinal Survey of Youth 1997 data and restricted comparison group, individual fixed effects, and sibling fixed effects models to examine residential mobility and temporary housing residence during early adulthood.
Results
I find robust evidence that never-incarcerated individuals with felony convictions experience elevated risk of housing instability and residential mobility, even after adjusting for important mediators like financial resources and relationships. The evidence that incarceration has an additional, independent effect on housing instability is weaker, however, suggesting that the association between incarceration and housing instability found in prior studies may largely be driven by conviction status.
Conclusions
These findings reveal that conviction, independent of incarceration, introduces instability into the lives of the 12 million Americans who have been convicted of a felony but never imprisoned. Thus, research that attempts to identify an incarceration effect by comparing outcomes to convicted individuals who receive non-custodial sentences may obscure the important independent effect of conviction. Moreover, these findings highlight that the socioeconomic effects of criminal justice contact are broader than incarceration-focused research suggests. Consequently, reform efforts promoting the use of community corrections over incarceration may do less to reduce the harm of criminal justice contact than expected.
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Notes
Some individuals may face residential geographic constraints as part of their probation or parole terms, and public housing authorities are required to deny applicants who are on the lifetime sex offender registry in any state or have been convicted of manufacturing methamphetamines on public housing property (Curtis et al. 2013).
The NLSY97 includes a nationally representative sample of 6748 respondents and a supplemental oversample of 2236 Hispanic and non-Hispanic black respondents.
The theft prompt includes auto theft, larceny, and shoplifting.
The prompt for “other property crimes” specifies “fencing, receiving, possessing or selling stolen property.”
Drug possession is the most serious charge in 14.6% of felony convictions in state courts (Rosenmerkel et al. 2009); therefore I have also run models with drug possession coded as a felony conviction. Results of these models are consistent with those shown here and are available upon request.
While the public order category can include felonies because weapons offenses are considered public order offenses, weapons convictions without an accompanying more serious felony conviction make up only 3 percent of all felony convictions in state courts (Rosenmerkel et al. 2009) and 8 percent of federal felony convictions (Schmitt and Jones 2017).
I have also run models in which I combine felony and non-felony incarcerations in a single previously incarcerated variable. Results from these models are substantively consistent with those presented here and are available upon request.
Because currently incarcerated respondents are by definition residing in a jail or prison, not one of the forms of temporary housing I consider, the currently incarcerated variable is perfectly collinear with the temporary housing outcome and, therefore, falls out of models of temporary housing residence.
In models that pull respondents with other convictions out into a separate category, I see that coefficients on previous felony conviction and previous felony incarceration increase slightly. Standard errors are little changed, and all results remain statistically significant. Results available upon request.
Results are consistent if I instead use zero-inflated Poisson and Firth penalized logistic regression models, respectively. While the number of moves variable is slightly overdispersed, negative binomial models produce nearly identical coefficients and standard errors as Poisson models. All results available upon request.
Controlling for age as a set of dummy variables avoids making assumptions about the functional form of the relationship between age, felony conviction risk, and housing instability risk. We know these relationships are highly age-graded and nonlinear, but the exact shape of the relationship between age and criminal activity, at least, differs across datasets. Moreover, by accounting for age as a set of dummy variables—essentially using age fixed effects—coefficients are estimated by comparing outcomes among respondents of the same age, which is recommended as best practice in attempting to estimate causal effects of criminal justice system contact using observational data (Nagin et al. 2009).
Some respondents reported exact dollar amounts for gift income, while others reported estimated amounts using seven ordinal response categories ranging from “$1–500” to “more than $10,000.” To adjust these values for inflation, I assign the midpoint of the reported range as the gift income amount in a given year, then adjust values to 2016 dollars. NLSY stopped asking respondents about gift income in 2013, so 2013, 2015, and 2017 values are imputed based on reported gift income amounts received in previous years along with all other covariates used in the full model. Models that exclude gift income or exclude observations from 2013–2017 in order to avoid the use of imputed gift income values produce results consistent with those shown in main tables.
NLSY97 collects data on the net worth of respondents and, if applicable, their spouse/partner in the first interview during or after the calendar year in which they turn 20, 25, 30, and 35. I subtract out the value of assets and debts that respondents report their spouses/partners do not share with them, then multiply impute net worth values for years in which assets are not collected.
I use OLS regression to fill in missing values on continuous variables, logistic regression for binary variables, ordinal logistic regression for ordinal variables, multinomial logit for nominal variables (e.g., household structure at age 14), and Poisson regression for count variables.
Standard errors are larger in the casewise deletion models because of smaller sample size, but the coefficients on previous felony conviction remain highly statistically significant across outcomes. The coefficients on previous felony incarceration, however, are no longer statistically significant in the full controls, sibling fixed effects, and high crime restricted comparison group models for either outcome when using casewise deletion.
Questions about gun carrying behavior are asked of all respondents in 1998–2011, and questions about marijuana and hard drug use are asked of all respondents in 1998–2011 and in 2015. Controlling for these variables in the years available produces results consistent with those shown in the main text.
Thirty-six percent of all NLSY97 respondents who are ever convicted of a felony by 2017 report their first conviction by age 20.
Because I cluster standard errors at the primary sampling unit (PSU) level, I am unable to cluster standard errors at the individual level in sibling fixed effect models to account for repeated observations of individuals. Results are consistent with those shown in the main table, however, when I cluster standard errors at the individual rather than PSU level in sibling fixed effect models.
I apply custom longitudinal sampling weights (https://www.nlsinfo.org/weights/nlsy97) when calculating the descriptive statistics shown in Fig. 1 and Table 1, but I do not weight regression models. Results of weighted models are consistent with those presented in the main text and available upon request.
Because fixed effects models require within unit variation to produce estimates, respondents who never live in temporary housing and respondents from families in which no sibling ever reports living in temporary housing at or after age 20 drop out of the individual and sibling fixed effect models, respectively.
I include the urban residence control in the relationships and behavior mechanism group. When I pull out urban residence separately it does little to explain the relationship between prior felony conviction or incarceration and housing instability; its inclusion reduces the coefficient on previous felony conviction by about 2% and increases the coefficient on previous felony incarceration by 4.6% across both outcomes.
The hard drug use question text reads: “Excluding marijuana and alcohol, in the last twelve months, have you used any drugs like cocaine, crack, heroin, or crystal meth, or any other substance not prescribed by a doctor, in order to get high or to achieve an altered state?” NLSY97 does not ask respondents these questions in 2013 or 2017, which is why I do not include these variables in the main tables.
The four Census regions include Northeast, North Central, South, and Midwest.
I am unable to run race-interacted sibling and individual fixed effect models because of lack of within unit variation on race/ethnicity.
Same-sex sibling fixed effect models and single-sex individual fixed effect models reveal a similar finding: the magnitude of the relationship between felony incarceration and both forms of housing instability, especially temporary housing, appears to be larger for women than for men. Results available upon request.
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Acknowledgements
This project was supported by funding from the Joint Center for Housing Studies at Harvard University and the Harvard Multidisciplinary Program in Inequality and Social Policy. The author wishes to thank Bruce Western, Alexandra Killewald, Hope Harvey, Margot Moinester, Robert Sampson, Anna Haskins, participants in the Social Demography Seminar at the Harvard Center for Population and Development Studies, and the reviewers and editor for their thoughtful feedback on this paper.
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Bryan, B. Housing Instability Following Felony Conviction and Incarceration: Disentangling Being Marked from Being Locked Up. J Quant Criminol 39, 833–874 (2023). https://doi.org/10.1007/s10940-022-09550-z
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DOI: https://doi.org/10.1007/s10940-022-09550-z