Reviews the book "Accounting for Data Processing Costs," by Robert W. McGee.

ABSTRACT: This paper demonstrates that the treatment of self-services, their inclusion or exclusion, affects neither the decision relevance of the information made available by the reciprocal method of cost allocation nor the internal versus external acquisition decision made through the underlying technology matrix. Three propositions and proofs are provided, showing that there is a one-to-one transformation between the two treatments of self-services in each of the analyses.

The article presents a comment on the effect of chance variation on revenue and cost estimations for breakeven analysis. In traditional breakeven analysis, total revenue and total cost are represented by straight lines with one intersection which indicates the breakeven quantity output. The effect of quadratic revenue and cost curves on breakeven analysis was introduced. Two breakeven points for the quadratic model were derived by solving for the levels of output at which the regressed total revenue equals total cost. Regardless of whether a linear or non-linear breakeven model may be appropriate, however, the revenue and cost functions are often unknown. Givens illustrated the regression analysis, but did not consider the effect of regression forecasting errors. In order to resolve the above issue an attempt was made to demonstrate the determination of what he called "the chance variation" in estimated revenue and cost functions; and to illustrate how these chance variations affect the results of a breakeven analysis. The estimated cost-volume functions were subtracted from the estimated revenue-volume functions to derive an estimated profit-volume function.

There is often evidence of confusion between two forms of the constant percentage learning curve model: the cumulative average and the individual unit forms. Failure to distinguish between the two models can lead to their misuse and to potentially serious errors of estimation. A precise statement of the difference between the two clarifies the errors of misspecification. This note provides an analytical comparsion of the two models and addresses empirical estimation issues. Résumé. On a souvent la preuve de la confusion entre deux formules de modèle de la courbe d'apprentissage à pourcentage constant: celle de la moyenne cumulative et celle de l'unité individuelle. Le fait de ne pas distinguer les deux modèles peut conduire à une mauvaise utilisation et éventuellement à de sérieuses erreurs d'estimation. Un examen précis de la différence entre les deux peut clarifier les erreurs de spécification. Cet article présente une comparaison analytique des deux modèles et traite des questions d'estimation empirique.

Reviews the book "The Capital Expenditure," by Arthur V. Corr.

Studies of the impact of migration on sending households (e.g., Dean Yang, 2004; Alejandra C. Edwards and Manuelita Ureta, 2003) have largely neglected the fact that certain allocations can only be imperfectly monitored when household members are not coresident (see Ralph Chami et al., 2003, for an exception). In this case, allocations can be coordinated only to the extent that they can be verified, and household decision making may not be fully cooperative. The existence of such behavior among household members would suggest that expanding opportunities for migration will have different effects on expenditure patterns than simply increasing the amount of income received by the household. Changes in earned income and the potential to earn income will affect bargaining among spouses, but noncooperative behavior will have an additional effect on the final distribution of household resources. With the

Rainfall measures may be imperfect proxies for floods, given factors such as upstream water balance, proximity to rivers, and topography. We check the robustness of flooding-migration relationships by combining nationally-representative survey data with measures of flooding derived from weather stations, gridded products, and remote sensing tools. Linear probability models reveal that extreme flooding is negatively associated with out-migration. Rainfall-based proxies produce results qualitatively similar to those using the satellite-based measure of inundation, but only the latter is able to discern non-monotonic effects throughout the distribution. Moreover, estimates differ widely across areas, suggesting that households respond differently to rainfall and flooding.