TAKING ORGANIZATIONS SERIOUSLY: RESPONSIBLE AGENCY, ORGANIZATIONS AND THE CRIMINAL LAW

In the post-Enlightenment period, Anglo-American criminal law has been applied with increased force, and an ever expanding scope, to collective actors like corporations and other organizations. Recent scholarship has focused on developing “truly organizational” bases of liability that break with the conventional approach of imputing individual conduct to an organization and instead analyze culpable conduct and intent in a way that reflects the distinct and independent capacity of organizations to pursue their interests or goals collaboratively. In 2004, Canada enacted amendments inspired by these ideas in the hope they would lead to more effective criminal enforcement against organizations. Twelve years later, however, the promise of Bill C-45 is largely unfulfilled. In this thesis, I explore how much of this failure of law reform to deliver transformational change is attributable to an individualist bias that permeates how we think about what it means to be responsible and how this then shapes the responsibility ascription process. Using an analytical framework that combines criminal law theory with selected aspects of rational-structural theory and organization culture, I suggest that a promising way forward may lie in reframing the essential qualities required to be a subject of the criminal law in a way that captures the unique attributes that make organizations different from individuals. The resulting organizational concept of responsible agency allows for an integration of organizational reality into how we assess organizational culpability while keeping the ambit of criminal liability within the limits of what is practicable and fair. This better aligns with the spirit of Bill C-45: to impose criminal liability in a way that takes organizations – and their crimes – seriously.

Keeping Variables within Bounds: Using Information between Observations

This research develops an econometric framework to analyze time series processes with bounds. The framework is general enough that it can incorporate several different kinds of bounding information that constrain continuous-time stochastic processes between discretely-sampled observations. It applies to situations in which the process is known to remain within an interval between observations, by way of either a known constraint or through the observation of extreme realizations of the process. The main statistical technique employs the theory of maximum likelihood estimation. This approach leads to the development of the asymptotic distribution theory for the estimation of the parameters in bounded diffusion models. The results of this analysis present several implications for empirical research. The advantages are realized in the form of efficiency gains, bias reduction and in the flexibility of model specification. A bias arises in the presence of bounding information that is ignored, while it is mitigated within this framework. An efficiency gain arises, in the sense that the statistical methods make use of conditioning information, as revealed by the bounds. Further, the specification of an econometric model can be uncoupled from the restriction to the bounds, leaving the researcher free to model the process near the bound in a way that avoids bias from misspecification. One byproduct of the improvements in model specification is that the more precise model estimation exposes other sources of misspecification. Some processes reveal themselves to be unlikely candidates for a given diffusion model, once the observations are analyzed in combination with the bounding information. A closer inspection of the theoretical foundation behind diffusion models leads to a more general specification of the model. This approach is used to produce a set of algorithms to make the model computationally feasible and more widely applicable. Finally, the modeling framework is applied to a series of interest rates, which, for several years, have been constrained by the lower bound of zero. The estimates from a series of diffusion models suggest a substantial difference in estimation results between models that ignore bounds and the framework that takes bounding information into consideration.