Date of Conferral





Public Policy and Administration


Mary D. Bruce


Predictive applications of a debarment list involve gathering historical data in the list to capture the relationships between the relevant variables in the data to predict the most likely future outcomes. Exploring whether the data in the debarment list could produce predictive analytics, which agencies may use to deter contractors from committing fraud, is unknown. This study closed the literature gap through a quantitative nonexperimental analysis of secondary data, inspired by real-life administrative decisions. The purpose of this study was to analyze the City of Chicago's debarment list to determine the statistical probability of business entities that may be debarred from receiving contract awards from the City. The study's theoretical foundation was predicated on deterrence theory, with a conceptual framework that offered a practical explanation of the dynamics of the debarment deterrence sanction system. The number of debarred contractors sampled from the City's debarment list in the fiscal year 2008 to 2019 was N = 138. Results of binomial logistic regression showed that procurement fraud is 50.7% as likely as to cause a firm debarred from receiving contracts from the City compared with an individual. However, procurement fraud is 72.60% as likely as to cause the City to debar an individual from receiving City contracts compared with a firm. The model showed that phony company fraud is 21.3 times more likely than contract fraud to trigger a firm's debarment. The predictions in this study have social implications for strengthening the use of debarment for fraud prevention, public advocacy, and better public funds management and positive social change.