Predicting Recidivism Using Survival Models

Schmidt, Peter; Witte, Ann Dryden

doi:10.1007/978-1-4612-3772-3

Cited by 163 publications

(156 citation statements)

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“…Research has consistently confirmed that the likelihood of failure is not uniform across the post-prison release period (Langan & Levin, 2002;Schmidt & Witte, 1988). However, very little is known about time to failure using a female prison population, as most existing research has been conducted using shortterm, dichotomous measures of recidivism.…”

Section: Dependent Variablesmentioning

confidence: 99%

Women Coming Home: Long‐Term Patterns of Recidivism

Huebner¹,

DeJong²,

Cobbina³

2009

Justice Quarterly

149

147

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Section: Dependent Variablesmentioning

confidence: 99%

Women Coming Home: Long‐Term Patterns of Recidivism

Huebner¹,

DeJong²,

Cobbina³

2009

Justice Quarterly

149

147

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“…Data to estimate the recidivism models were obtained from the Inter-University Consortium of Political and Social Research. These data comprise the 1980 North Carolina release cohort data set described in Schmidt and Witte (1988). This data set (henceforth referred to as the S&W sample) contains recidivism information for 9,549 prisoners released from North Carolina prisons between July 1, 1979, and June 30, 1980.…”

Section: Data and Estimation Methodsmentioning

confidence: 99%

“…The lognormal failure function was chosen because the hazard rate of this distribution, which increases and then decreases, is consistent with observed recidivism patterns (see, for example, Schmidt and Witte, 1988). As the hazard rate associated with the lognormal distribution is not constant, the probability of returning to prison at time t is conditional on the time since release.…”

Section: : Omentioning

confidence: 96%

“…Between 1980 and1988, the number of inmates in state and federal prisons increased more than 90 percent, from 329,821 to 627,402 (US Department of Justice, Bureau of Justice Statistics, 1989). At the end of 1992,43 state jurisdictions and the Federal prison system were operating at 100 percent or more of their prison capacities (US Department of Justice, Bureau of Statistics, 1993, p. 6).…”

Section: Introductionmentioning

confidence: 99%

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Forecasting Demand Using Survival Modeling: an application to US prisons

Baker

Lattimore

1994

AJIS

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A systems approach to modeling demand which incorporates survival modeling is applied to the problem of prison population projection. The approach models the flow of inmates through the prison system and differs from earlier approaches by exploiting the differences in the incarceration hazard rates of individuals in the general population and those who have previously been incarcerated and explicitly considering the impact of constrained prison capacity on release policy and future admissions. The methodology capitalizes on the impact of recursion in the prison population and reduces the amount and complexity of data required for long-term forecasts.. First-time arrivals to prison are modeled as a Poisson process arising from the general population; recidivist arrivals are modeled using a failure model, where the reincarceradon hazard rate is a function of age and race. The model is demonstrated for the state of North Carolina located in the Southeastern region of the United States. The effect of limited prison capacity on the mean of the time-served distribution is shown. The results demonstrate that an early release policy will generate an increase in prison admissions through the return to prison of former inmates. Further, the results show that a systems approach to modeling of prison demand which includes the non-linear effect of recidivism, i.e., survival modeling, has a significant impact on the accuracy of forecasts.

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“…This would be true, for example, if the factors determining the seriousness of offending and the probability were different. Schmidt and Witte (1988) have recently experimented with models that allow the probability and the timing of offenses to be affected by explanatory variables in different ways. It is interesting that they were able to explain the probability of offending to a much greater degree than its timing.…”

Section: The Empirical Modelmentioning

confidence: 99%

Deterrence, Work and Crime: Revisiting the Issues with Birth Cohort Data

Tauchen

Witte

Griesinger

1988

Self Cite

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In this paper we analyze the criminal behavior of a cohort sample of young men over an eight year period using random effects probit and Tobi techniques. Our major advances relate to our careful conceptualization of general deterrence, and our data. As far as we are aware, this work represents the first time that a richly specified model of criminal activity has been estimated using panel data for a general population group.We find very robust evidence for a general deterrent effect emanating from police resources. Our results regarding general deterrence are open to fewer questions than previous findings. We also find that working and going to school significantly decrease the probability of committing criminal acts and by virtually identical amounts. This similarity of effect when coupled with other findings suggests that crime does not serve mainly as a direct source of income and that incentive effects emanating from higher wages are not very strong. There is little empirical support for the "crime as work" model that has dominated economic thought regarding crime over the last two decades. More fruitful models of work and crime may result if work is conceived as having its primary effects either through preferences or -through information.

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Predicting Recidivism Using Survival Models

Cited by 163 publications

References 0 publications

Women Coming Home: Long‐Term Patterns of Recidivism

Women Coming Home: Long‐Term Patterns of Recidivism

Forecasting Demand Using Survival Modeling: an application to US prisons

Deterrence, Work and Crime: Revisiting the Issues with Birth Cohort Data

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