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Sowing the seeds: how adult incarceration promotes juvenile delinquency

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Abstract

Informed by recent research on the collateral consequences of the wars on crime and drugs, we hypothesize that high levels of adult incarceration are associated with high levels of juvenile delinquency. We test this hypothesis using panel data for North Carolina counties covering the years 1995 to 2009. A series of comprehensive regression models indicates a significant positive accelerating relationship between adult imprisonment and juvenile arrest rates (holding constant the prevalence of adult arrests and other factors). The results suggest that adult imprisonment rates are only linked to juvenile delinquency in the context of what has been called “mass imprisonment” or “hyper-incarceration.”

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Notes

  1. The present paper focuses on the notion that incarceration’s detrimental effects will be scale dependent. That is, we empirically examine the idea that certain problematic dynamics are only initiated at very high levels of incarceration. Some recent research [41, 42] suggests that the criminogenic impact of criminal justice interventions will also be time dependent (e.g. short-term versus longer-term effects). We leave the determination of the temporal dependency of the incarceration-delinquency relationship for future research.

  2. The data are found at http://data.osbm.state.nc.us/pls/linc/dyn_linc_main.show.

  3. White’s test indicated the presence of heteroskedasticity, while regression-based tests found first-order serial correlation in the errors (.3412; p < .001). White’s test was done using the whitetst routine in Stata 11.

  4. Estimation of both the OLS and TSLS models is done using the ivreg2 routine in Stata 11. The program permits the calculation of the corrected standard and the generalized method of moments estimator for the TSLS models. Use of the so-called Newey–West [47] standard errors requires the selection of the lag length for autocorrelation correction. We tried lag lengths up to 3 and found that the conclusions from the hypothesis tests are not sensitive to the choice.

  5. The practice of including a lagged dependent variable in a panel model with fixed effects is problematic. As pointed out by Nickell [49], doing so induces simultaneous equation bias even when none existed before, although the size of the bias decreases as the sample size increases [see also, [50]]. If a lagged dependent variable is included under these circumstances, it is preferable to use an alternative estimation strategy specifically developed to handle the situation, such as Arellano and Bond [51].

  6. In order to reduce non-essential ill conditioning [43], the incarceration rate is centered at zero before the squared term is calculated. The centered rate and its square are used in the regressions.

  7. Although the three social disorganization variables had relatively small pairwise correlations, we formed a “disorganization index” equal to the sum of the z-scores for the three variables. The estimated coefficient on the index was positive but insignificant and did not change the estimates for the incarceration rate or its square.

  8. The estimation was done using the rreg routine of Stata 11.

  9. Whether or not the constructed instruments are uncorrelated with the error in Eq. 1, and thus are valid, is an empirical issue that can be formally tested. We conduct the appropriate tests and report on the results below.

  10. That is, the variables explain a substantial and acceptable level of variation in the incarceration rate and its square, and the instruments are uncorrelated with the error term in the structural equation (Eq. 1). Concerning effectiveness, the F statistics for the two instrument equations are over thirty, more than three times the standard cutoff for acceptability [53]. Validity is indicated by the Hansen J-statistic, which is too low to reject the null hypothesis that the instruments are uncorrelated with the error term (p = .2347). See Wooldridge (2009) for a discussion of the relevant tests.

  11. Hausman pointed out that TSLS estimates are always consistent (with efficient and valid instruments), so one can compare the magnitudes of the OLS and TSLS coefficients. If they are close to one another, one should conclude that there is no meaningful bias and rely on the OLS estimates. If they are different, then bias exists and TSLS estimates should be used.

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  1. Department of Sociology and Criminal Justice, Villanova University, 800 Lancaster Ave., Villanova, PA, 19085, USA

    Lance Hannon & Robert DeFina

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  1. Lance Hannon
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  2. Robert DeFina
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Correspondence to Lance Hannon.

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Lance Hannon and Robert DeFina contributed equally to the article.

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Hannon, L., DeFina, R. Sowing the seeds: how adult incarceration promotes juvenile delinquency. Crime Law Soc Change 57, 475–491 (2012). https://doi.org/10.1007/s10611-012-9374-1

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