Recidivism and Social Interactions

Using a national sample, this article identifies the risk factors for recidivism among female, male, black, white, and Hispanic felony probationers. The individual hazard function is assumed to depend on individual and neighborhood characteristics, as well as on social interactions among probationers. In selecting the covariates from a set of potential candidates, Bayesian model averaging is used to account for both model uncertainty and the subsequent inference. The results point to social interactions as one of the most significant factors affecting recidivism among all gender, ethnicity, and race groups. When a frailty parameter is added to account for the possibility of unobserved risk factors shared by probationers within neighborhoods, the empirical results remain robust indicating negligible unobserved neighborhood-level heterogeneity.

[1]  J. Lawless,et al.  Efficient Screening of Nonnormal Regression Models , 1978 .

[2]  Christy A. Visher,et al.  PREDICTING THE RECIDIVISM OF SERIOUS YOUTHFUL OFFENDERS USING SURVIVAL MODELS , 1991 .

[3]  J. Berger,et al.  Testing Precise Hypotheses , 1987 .

[4]  D. Madigan,et al.  Bayesian model averaging and model selection for markov equivalence classes of acyclic digraphs , 1996 .

[5]  L. Wasserman,et al.  Computing Bayes Factors by Combining Simulation and Asymptotic Approximations , 1997 .

[6]  D. Madigan,et al.  Bayesian Model Averaging in Proportional Hazard Models: Assessing the Risk of a Stroke , 1997 .

[7]  Lin Huff-Corzine,et al.  Return to the Scene of the Punishment: Recidivism of Adult Male Property Offenders on Felony Probation, 1986-1989 , 1997 .

[8]  N. Breslow,et al.  Analysis of Survival Data under the Proportional Hazards Model , 1975 .

[9]  J. Whitehead.  The effectiveness of felony probation: Results from an eastern state , 1991 .

[10]  D. Madigan,et al.  Model Selection and Accounting for Model Uncertainty in Graphical Models Using Occam's Window , 1994 .

[11]  James P. Kahan,et al.  Granting Felons Probation: Public Risks and Alternatives , 1985 .

[12]  D. Rubin,et al.  Multiple Imputation for Nonresponse in Surveys , 1989 .

[13]  I. Deutscher,et al.  Success and Failure , 1988 .

[14]  Jeffrey T. Grogger,et al.  The Spatial Concentration of Crime , 1996 .

[15]  D. A. Pritchard.  STABLE PREDICTORS OF RECIDIVISM , 1979 .

[16]  E. Glaeser,et al.  Crime and Social Interactions , 1995 .

[17]  D.,et al.  Regression Models and Life-Tables , 2022 .

[18]  C. A. Young.  OBSERVATIONS UPON THE COMET AT PRINCETON. , 1881, Science.

[19]  C. Chatfield.  Model uncertainty, data mining and statistical inference , 1995 .

[20]  Gilles Cauffet,et al.  Magnetization identification problem: Illustration of an effective approach , 2004 .

[21]  Guang Guo,et al.  Estimating a Multivariate Proportional Hazards Model for Clustered Data Using the EM Algorithm, with an Application to Child Survival in Guatemala , 1992 .

[22]  Joan Petersilia,et al.  Comparing Intensive and Regular Supervision for High-Risk Probationers: Early Results from an Experiment in California , 1990 .

[23]  A. Kuk.  All subsets regression in a proportional hazards model , 1984 .

[24]  G. LaFree.  Official Reactions to Hispanic Defendants in the Southwest , 1985 .

[25]  P. Grambsch,et al.  Penalized Survival Models and Frailty , 2003 .

[26]  David Draper,et al.  Assessment and Propagation of Model Uncertainty , 2011 .

[27]  M. Sobel,et al.  Identification Problems in the Social Sciences. , 1996 .

[28]  Anthony Ralston,et al.  Mathematical Methods for Digital Computers , 1960 .

[29]  A. Raftery.  Approximate Bayes factors and accounting for model uncertainty in generalised linear models , 1996 .

[30]  D. Madigan,et al.  Bayesian Model Averaging for Linear Regression Models , 1997 .

[31]  W. Brock,et al.  Interactions-Based Models , 2000 .

[32]  Robert W. Wilson,et al.  Regressions by Leaps and Bounds , 2000, Technometrics.

[33]  M. Rorty.  Unemployment Insurance and Unemployment , 1936 .

[34]  Adrian E. Raftery,et al.  MODEL SELECTION AND ACCOUNTING FOR MODEL UNCERTAINTY IN LINEAR REGRESSION MODELS , 2007 .

[35]  Johnny Mcgaha,et al.  Felony probation: A re-examination of public risk , 1987 .

[36]  Bruce D. Meyer.  Unemployment Insurance and Unemployment Spells , 1988 .

[37]  William A. Brock,et al.  Discrete Choice with Social Interactions , 2001 .

[38]  G. Schwarz.  Estimating the Dimension of a Model , 1978 .

[39]  Reuel Shinnar,et al.  The Effects of the Criminal Justice System on the Control of Crime: A Quantitative Approach , 1975 .

[40]  J. Heckman,et al.  A Method for Minimizing the Impact of Distributional Assumptions in Econometric Models for Duration Data , 1984 .

[41]  J. Kahan,et al.  Executive Summary of Rand's Study, “Granting Felons Probation: Public Risks and Alternatives” , 1985 .

[42]  G. King,et al.  Analyzing Incomplete Political Science Data: An Alternative Algorithm for Multiple Imputation , 2001, American Political Science Review.

[43]  R. Sah.  Social Osmosis and Patterns of Crime , 1991, Journal of Political Economy.