The 10% Flat Tax: Tithing and the Definition of Income

Developing a fair and widely accepted income definition presents one of the greatest challenges to tax reform. To arrive at a definition separate from the federal tax code, we surveyed 1200 Latter‐day Saints about their practice of tithing. Tithing is similar to a flat tax with no deductions, where individuals voluntarily contribute 10% of self‐defined income to the church. The results of our survey indicate that most respondents operate on a cash realization basis, which excludes savings and does not allow any deductions. Respondents' income concepts generally do not coincide with current tax laws or economists' views of comprehensive income.     

OPTIMIZING ORGANIZATIONAL GOALS IN ASSIGNING FACULTY TEACHING SCHEDULES

The administration of higher education, as with other administrative environments, has become increasingly complex. This paper presents a mathematical model which addresses one of the administrative tasks concerning administrators in all institutions, the establishment of faculty teaching schedules. The assignment technique utilized by the model is linear programming with two special characteristics: goal programming and mixed-integer programming. The goal programming characteristic refers to the provision of explicit stack variables to take on values representing deviations from assignment criteria that may result in resolving conflicts which arise from interactive administrative priorities. The mixed-integer programming characteristic refers to a requirement of the model that certain variables take on only integer values if they appear in the final solution. The model is demonstrated using two different sets of preference orderings for goal achievement.     

BUSINESS COMBINATION DECISIONS: A U.S./CANADA STUDY*

The purpose of this paper is to clarify the motives behind business combination decisions. Twenty-one favorable characteristics associated with the motivations for business combinations were selected to serve as the basis for an empirical survey. Our analysis revealed that there are seven motivational variables of varying import and that the motivations underlying business combination decisions are similar for U.S. and Canadian executives.     

ESTIMATING A PARAMETER WHEN IT IS KNOWN THAT THE PARAMETER EXCEEDS A GIVEN VALUE

In some statistical problems a degree of explicit, prior information is available about the value taken by the parameter of interest, θ say, although the information is much less than would be needed to place a prior density on the parameter's distribution. Often the prior information takes the form of a simple bound, ‘θ > θ₁ ’ or ‘θ < θ₁ ’, where θ₁ is determined by physical considerations or mathematical theory, such as positivity of a variance. A conventional approach to accommodating the requirement that θ > θ₁ is to replace an estimator, , of θ by the maximum of and θ₁. However, this technique is generally inadequate. For one thing, it does not respect the strictness of the inequality θ > θ₁ , which can be critical in interpreting results. For another, it produces an estimator that does not respond in a natural way to perturbations of the data. In this paper we suggest an alternative approach, in which bootstrap aggregation, or bagging, is used to overcome these difficulties. Bagging gives estimators that, when subjected to the constraint θ > θ₁ , strictly exceed θ₁ except in extreme settings in which the empirical evidence strongly contradicts the constraint. Bagging also reduces estimator variability in the important case for which is close to θ₁, and more generally produces estimators that respect the constraint in a smooth, realistic fashion.