Prior studies of the strategic interaction between taxpayers and the tax authority have focused on reported net taxable income and on audit policies designed to discover potential misstatement of that single item. This paper extends the literature by modeling taxpayer compliance behavior and tax authority audit strategies within the context of a multidimensional report of taxable income. Specifically, the study analyzes the impact of component reporting requirements on taxpayer incentives to misstate their tax liability. It also allows the tax authority to tailor its audit policy to consider all tax return information. In particular, the model permits the tax authority to audit return components sequentially: the investigation of a second component is conditional on the results of the first component's audit. The study finds that partitioning taxable income into a multi-component report reduces overall tax evasion and increases tax authority net revenue collections relative to a singlereport model of net taxable income. However, the impact on predicted evasion is not uniform across taxpayers. While some taxpayers reduce evasion, others with multiple opportunities to evade are more likely to do so when faced with multi-component reporting requirements.

Tax professionals provide valuable services to clients by reducing uncertainty about how clients should report transactions on their tax returns. To reduce uncertainty, tax professionals research applicable authorities (e.g., judicial precedents) and provide assessments to clients of the level of authoritative support for client-favorable positions. Tax professionals have strong incentives to make accurate assessments of the strength of client-preferred positions so that clients will understand the level of risk associated with the reporting position. Further, tax professionals must make accurate assessments of authoritative support in order to maintain compliance with tax professional standards and Federal income tax regulations. Incentives notwithstanding, psychological research on confirmation bias suggests that tax professionals' client advocacy role may inhibit professionals' ability to search objectively for relevant tax authority which, in turn, might inhibit their ability to accurately assess authoritative support. We report the results of two studies that examine causes and effects of confirmation bias in tax information search. In study 1, we find that subjects' information searches emphasized cases with conclusions consistent with the client's desired outcome (i.e., positive cases) over cases inconsistent with the client's desired outcome (i.e., negative cases), despite the fact that positive cases were no more similar to the client's facts. Additional analyses indicate that the extent of this confirmation bias was positively related to their assessments of the likelihood that a neutral court would resolve the issue in the client's favor and this in turn increased the strength with which they recommended the client's preferred tax position. Results of study 2 indicate that confirmation bias induced by client preferences can be strong enough to not only result in inaccurate assessments of authoritative support for the client-favored position, which is problematic in and of itself, but also to lead tax professionals to make overly aggressive recommendations.

Accounting researchers (and potentially others) generally select rather simple, lower-order, time-series models to develop proxies for earnings persistence. However, measures of persistence produced by such models are not related to characteristics of the firm's economic environment that are expected to influence earnings persistence. Using a sample of 162 calendar year-end New York Stock Exchange firms, we document the cross-sectional relations between a set of relatively constant, firm-specific, economic characteristics that are theoretical determinants of persistence and measures of earnings persistence derived from both lower-order and higher-order Autoregressive, Integrated, Moving-Average (ARIMA) models. When lower-order ARIMA models are used to generate measures of earnings persistence, the cross-sectional regression models measuring the association between persistence and economic determinants of persistence yield very low adjusted R2s. In sharp contrast, when differenced, higher-order ARIMA models are used to measure earnings persistence, adjusted R2s are in the 10–12 percent range. Moreover, independent variables such as capital intensity, barriers-to-entry, and product-type are all significant in the directions suggested by economic theory. Our results are consistent with Lipe and Kormendi (1994) who argue that higher-order ARIMA models do a better job of capturing the valuerelevance of current period earnings than lower-order models.