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The classification of a financial instrument as "debt" or as "equity" is crucial in applying a wide range of income tax provisions. "Interest" expenses incurred on "debt," for example, are deductible in computing a firm's taxable income, whereas "dividends" paid on the firm's outstanding "equity" are not. "Interest" paid by a U.S. corporation to a foreign creditor is generally not subject to U.S. withholding taxes, whereas "dividends" paid on stock held by a foreign shareholder are typically subject to U.S. withholding taxes that range from 5% to 30% of the gross amount of the dividend paid. And the list goes on . . . . Not surprisingly, these disparities in tax treatment have inspired a plethora of schemes, many of them successful, designed to disguise equity investments as "debt." The urge to do so is perhaps nowhere more intense than in situations where the "debt" of a U.S. corporation is held by a foreign sister corporation located in a tax haven country. Interest paid or accrued on debt would be deductible in computing the U.S. corporation's U.S. taxable income, and would thereby permanently reduce the debtor's U.S. tax liability. The interest income earned by the foreign creditor would be exempt from both U.S. withholding taxes and income taxes in the foreign tax haven country. This interdisciplinary case was developed from the facts and circumstances before the U.S. Tax Court in litigation that resulted from the government's assertion that $975 million in "loans" made by a wholly owned Dutch subsidiary of Laidlaw Transit, Ltd. to several of Laidlaw's U.S. subsidiaries were in substance "equity." As in most debt-versus-equity cases, the stakes were high: Laidlaw's U.S. subsidiaries had deducted over $133 million of intercompany "payments" made to their Dutch sister corporation as "interest expense" and the IRS was suing to recover $52 million in back taxes (plus interest and penalties). This case integrates three disciplines – tax accounting, financial accounting, and finance -- in an easy-to-comprehend, yet rich setting appropriate for general management, finance, and accounting audiences. It invites students to thoroughly explore the substance-over-form doctrine as it applies to the debt-versus-equity issue, together with many of the tax, financial, accounting, and economic ramifications that flow from an instrument's classification. It also provides students with an opportunity to identify the ethical issues that attend the formulation and implementation of many tax minimization strategies and to identify factors that separate legal "tax avoidance" from criminal "tax evasion."

The ability to collect and disseminate individually identifiable microdata is becoming increasingly important in a number of arenas. This is especially true in health care and national security, where this data is considered vital for a number of public health and safety initiatives. In some cases legislation has been used to establish some standards for limiting the collection of and access to such data. However, all such legislative efforts contain many provisions that allow for access to individually identifiable microdata without the consent of the data subject. Furthermore, although legislation is useful in that penalties are levied for violating the law, these penalties occur after an individual's privacy has been compromised. Such deterrent measures can only serve as disincentives and offer no true protection. This paper considers security issues involved in releasing microdata, including individual identifiers. The threats to the confidentiality of the data subjects come from the users possessing statistical information that relates the revealed microdata to suppressed confidential information. The general strategy is to recode the initial data, in which some subjects are "safe" and some are at risk, into a data set in which no subjects are at risk. We develop a technique that enables the release of individually identifiable microdata in a manner that maximizes the utility of the released data while providing preventive protection of confidential data. Extensive computational results show that the proposed method is practical and viable and that useful data can be released even when the level of risk in the data is high.