Earnings management (EM) and white-collar crime (WCC) have been associated with corporate scandals. Companies involved in fraudulent financial reporting have used deceitful practices to manipulate earnings and hide investors from the true natures of their financial conditions. The purpose of this quantitative study was to examine what relationship, if any, exists between EM and WCC and to determine if multiple investigative approaches (financial ratios, corporate governance factors, and cash flow measures) could spot EM and WCC before they occur. The study was informed by seminal white collar crime theories of sociologist Sutherland and other scholars including Tillman, Bucy and colleagues. Archival data were used to select fraudulent firms from accounting and auditing enforcement releases (AAERs) issued in 2006 (N = 41). Financial statement data from the sample companies were collected from the annual reports submitted to the Securities and Exchange Commission. Fraudulent firms were matched to non-fraudulent firms based on firm size, time period, and industry. Using a matched-pair design, 11 hypotheses were generated to provide insight into the different components that drive EM. A logistic regression model was used to determine the relationship between EM and WCC in the fraudulent firms. One financial ratio, (Sales/Accounts Receivable), and two corporate governance factors (Insiders Control > 50% and Chief Executive Officer = Chairman of the Board) were found to be statistically significant predictors of EM and WCC. Identifying fraudulent activity before it occurs will facilitate positive social change by reducing the adverse economic and social effects of EM and WCC on the financial markets and thereby help restore public trust and confidence in the American financial systems.
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