Capacity as philosophy: review of Lippke's Ethics of Plea Bargaining

Plea bargaining is a response to capacity overload in the criminal justice system. It both preserves and belies the right to trial, making possible its glorious display but only by denying it in most cases. While plea bargaining has been documented and analysed copiously in historical, sociological and legal terms, its ethical status as an institutional practice are hazy. Richard Lippke offers an account of plea bargaining that draws on the normative debates over responsibility, culpability and desert, in aid of a holistic proposal for a morally defensible system of pre-trial adjudication. In proposing an ethical system of plea bargaining, and working through the normative challenges to this, two bigger questions become visible. These are: what are the implications of developing, in essence, an ethics of efficiency, and, how should the criminal justice system be held to account for the inequalities (and iniquities) that exist before and outside it? In this review essay, I show how these questions are constructed in the book and make some attempt at analysing them, thus engaging with the more urgent and general issue of the complicated relationship of the ideal to the real when it comes to penal practice

Explicating the Conditions Under Which Multilevel Multiple Imputation Mitigates Bias Resulting from Random Coefficient-Dependent Missing Longitudinal Data

Random coefficient dependent (RCD) missingness is a non-ignorable mechanism through which missing data can arise in longitudinal designs. RCD, for which we cannot test, is a problematic form of missingness that occurs if subject-specific random effects correlate with propensity for missingness or dropout. Particularly when covariate missingness is a problem, investigators typically handle missing longitudinal data by using single-level multiple imputation procedures implemented with long-format data, which ignores within-person dependency entirely, or implemented with wide-format (i.e., multivariate) data, which ignores some aspects of within-person dependency. When either of these standard approaches to handling missing longitudinal data is used, RCD missingness leads to parameter bias and incorrect inference. We explain why multilevel multiple imputation (MMI) should alleviate bias induced by a RCD missing data mechanism under conditions that contribute to stronger determinacy of random coefficients. We evaluate our hypothesis with a simulation study. Three design factors are considered: intraclass correlation (ICC; ranging from .25 to .75), number of waves (ranging from 4 to 8), and percent of missing data (ranging from 20% to 50%). We find that MMI greatly outperforms the single-level wide-format (multivariate) method for imputation under a RCD mechanism. For the MMI analyses, bias was most alleviated when the ICC is high, there were more waves of data, and when there was less missing data. Practical recommendations for handling longitudinal missing data are suggested