Previous research has established a consensus that in-hospital adverse events are ubiquitous, cause significant harm to patients, and have important financial consequences. However, information on the extent, consequences and costs of adverse events in Canada is limited. For example, there is, as yet, no published study that has investigated the costs of adverse events in a Canadian context. This dissertation aims to redress this situation by providing Ontario-based estimates of the impact of eleven nursing sensitive adverse events on cost, death, readmission, and ambulatory care use within 90 days after hospitalization. This dissertation also aims to contribute more broadly to the patient safety literature by quantifying the impact of diagnostic coding error in administrative data on estimates of the excess costs attributable to adverse events. Given the increasing importance of these estimates in Canada and elsewhere for hospital payment policy and for assessments of the business case for patient safety, this is an important gap in the literature. Each of the adverse events was associated with positive excess costs, ranging from $29,501 (metabolic derangement) to $66,412 (pressure ulcers). Extrapolation from the study hospitals yielded a provincial estimate of $481 million in annual excess costs attributable to the adverse events, which represents 2.8 percent of Ontario’s total hospital expenditures. Several of the adverse events were also associated with significant excess rates of death, readmission, and ambulatory care use. These results suggest that there are economic as well as ethical reasons to improve patient safety in Ontario hospitals. Estimates of adverse event costs were highly sensitive to coding error. The excess cost of adverse events is likely to be significantly underestimated if the error is ignored. This finding, coupled with the observation that the likelihood of error is ignored in most studies, suggests that previous assessments of the business case for patient safety may have been biased against the cost effectiveness of patient safety improvements. Furthermore, the observed extent of institutional level variation in adverse event coding indicates that administrative data are an inadequate basis for adverse event payment policies or for public reporting of adverse event rates.

WHAT DRIVES FIRMS TO DIVERSITY? By RONG GUO Committee Chair: Dr. Omesh Kini Major Department: Finance This paper examines whether corporate governance structures, serving as proxies for agency costs, can explain firms’ decision to diversify. Specifically, it has been hypothesized that firms with worse corporate governance structures are more likely to diversify. The extant literature usually compares the governance characteristics of multi-segment firms to those of single segment firms to address this issue. However, different governance characteristics may simply reflect differences in firm characteristics of diversified firms and focused firms. Furthermore, industry factors may affect both the propensity of firms to diversify and their governance characteristics. To separate out the agency costs explanation of firms’ decision to diversify, I compare the corporate governance structures of single segment firms that choose to diversify with those of a matched sample of single segment firms in the same industry that choose to remain focused. I find that firms with a higher percentage of outsiders on the board and smaller board size are more likely to diversify. These findings are inconsistent with the agency costs explanation of why firms choose to diversify. In addition, the CEO pay-to-performance sensitivity of diversifying firms is also not significantly different from that of firms that stay focused. The corporate governance characteristics cannot explain the changes in excess value around diversification either. Although some of the governance characteristics are significantly related to the announcement effects of diversifying mergers, these relations are often inconsistent with the agency cost explanation. Taken together, my evidence indicates that diversifying firms do not systematically have worse governance structures than firms that stay focused and, therefore, higher agency costs do not appear to drive the decision to diversify.

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The state transportation agencies (STAs) in the United States are mandated by federal rule to carry out work-zone impact assessment for highway rehabilitation projects. The work zone impact assessment requires calculating road user costs (RUCs) which is the sum of vehicle operating costs, accident costs, and value of time (VOT). The term ‘value of time’ refers to monetary equivalent of travel time wasted due to rehabilitation projects. In current practice, STAs assume VOT as homogeneous within their respective states. This leads to inaccurate RUCs calculations and poses many misapplications. Research has found that VOT is influenced by socio-demographic variables which vary within the states. But there is a lack of framework to evaluate the extent to which these factors affect value of time. The major objective of this research is to develop and validate a model that predicts value of time heterogeneously. The data were collected to cover 20 major cities in California. The state of California was chosen for this study because most highway rehabilitation projects are carried out there. The data sources included the United States Census Bureau, the California Department of Transportation (Caltrans), and the Bureau of Labor Statistics. With these data, a predictive model was developed using multiple linear regression analysis. Lastly, the model was validated using PRESS statistic. The results reveal that age, annual average daily traffic, and effective hourly income were the most significant factors influencing value of time. This study developed a model which will help Caltrans in calculating value of time heterogeneously and therefore, improve the accuracy of RUCs calculations. Moreover, this research will serve as a guideline for other STAs to develop models for respective states. Therefore, this model has a potential to greatly improve the accuracy of value of time and therefore, RUCs. The future research should focus on the identified factors, especially cost-of-living index and annual average daily traffic. Further research is required to account for heterogeneity due to other factors such as vehicle occupancy, frequency of travel, and educational qualifications.