The Role of Supervised Community Service and Socio-Economic Status in Recidivism Pertaining to Financial Crimes among Ex-Convicts

An individual’s economic situation impacts the commission of crimes, and ex-convicts inability to earn a living and integrate into society increases their propensity to commit financial crimes. Researchers indicate that the high rate of recidivism points to the fact that ex-convicts face significant challenges in their bid to adjust to life outside prison. Prior research and extant literature show that most ex-convicts re-offend within three years after their initial release from prison. Generally, the propensity to commit a financial crime increases after prison time among convicted felons. However, an elevated socio-economic status reduces an ex-convict’s propensity to commit financial crimes and recidivate. Therefore, it is expected that ex-convicts who participate in supervised community service will be less likely to commit financial crimes and recidivate. If most repeat offenses involve financial crimes, then recidivism can be significantly reduced by controlling the propensity to commit financial crimes among ex-convicts. This study employs a multivariate regression analysis to investigate a nationally aggregated archival data of paroled ex-convicts to determine the impact of socio-economic factors and supervised community service on ex-convicts’ inclination to commit financial crimes. The current study finds that elevated socio-economic status reduces financial crimes. However, there is no conclusive indication from the current study that supervised community service reduces recidivism pertaining to financial crimes among ex-convicts

Restricted estimation in multivariate measurement error regression model

We study a multivariate ultrastructural measurement error (MUME) model with more than one response variable. This model is a synthesis of multivariate functional and structural models. Three consistent estimators of regression coefficients, satisfying the exact linear restrictions have been proposed. Their asymptotic distributions are derived under the assumption of a non-normal measurement error and random error components. A simulation study is carried out to investigate the small sample properties of the estimators. The effect of departure from normality of the measurement errors on the estimators is assessed.Measurement error Multivariate regression Reliability matrix Linear restrictions Consistent estimators