Volunteering for Human Service Provisions: Lessons from Italy and the U.S.A.

The increased reliance on volunteers in all industrialized democracies has been parallelled by growing fiscal crises in most states, widespread criticism of welfare, and increased demand for social services. While volunteer work is presumed to be an alternative to public services, its feasibility is not yet clear. We suggest that a cross-national comparison of two significantly different countries would provide more information about volunteerism as a partial substitute for public services. We compared the United States where volunteerism is a widespread tradition and Italy where there has been a “rediscovery” of volunteerism since the 1980s. Differences between the two countries in the practice of volunteerism are examined from several perspectives. They include the relationships between volunteers and the statutory sector, the professionalization of volunteer activity, the role of citizen participation in a capitalistic society, and the Lockean principle of limited government. Finally, we conclude that while there are many differences in welfare provision between the United States and Italy, they do have a common element: increased reliance on volunteers for every aspect of day-to-day life; however, this reliance is mostly ideologically-based and may prove unfounded and costly.     

Regional—sub-cultural explanations of black fertility in the United States

This paper re-examines the relevance of the hypothesis concerning the Southern sub-culture and black fertility in the United States. According to zero-order correlation analysis, Southern-born blacks show higher fertility than those born in other regions. But a dummy-variable regression technique applied to the data from the 1970 public-use sample indicates that the Southern origin — independent of other factors-is not different from other regions. Southern birth has the effect of increasing black fertility, but much less so than birth in the North Central region. Southern blacks show higher fertility because of their lower socio-economic background. It is concluded that geographical regions in the United States have little or no cultural meaning in explaining reproductive behaviour.     

The Emigration of Pro-social Behaviors: A Comparative Analysis of Colombian and US Latter-day Saints

VOLUNTAS: International Journal of Voluntary and Nonprofit Organizations - Scholars have studied the differences in pro-social behaviors among countries for over 50 years. Religious...     

Pricing Software Upgrades: The Role of Product Improvement and User Costs

The computer software industry is an extreme example of rapid new product introduction. However, many consumers are sophisticated enough to anticipate the availability of upgrades in the future. This creates the possibility that consumers might either postpone purchase or buy early on and never upgrade. In response, many software producers offer special upgrade pricing to old customers in order to mitigate the effects of strategic consumer behavior. We analyze the optimality of upgrade pricing by characterizing the relationship between magnitude of product improvement and the equilibrium pricing structure, particularly in the context of user upgrade costs. This upgrade cost (such as the cost of upgrading complementary hardware or drivers) is incurred by the user when she buys the new version but is not captured by the upgrade price for the software. Our approach is to formulate a game theoretic model where consumers can look ahead and anticipate prices and product qualities while the firm can offer special upgrade pricing. We classify upgrades as minor, moderate or large based on the primitive parameters. We find that at sufficiently large user costs, upgrade pricing is an effective tool for minor and large upgrades but not moderate upgrades. Thus, upgrade pricing is suboptimal for the firm for a middle range of product improvement. User upgrade costs have both direct and indirect effects on the pricing decision. The indirect effect arises because the upgrade cost is a critical factor in determining whether all old consumers would upgrade to a new product or not, and this further alters the product improvement threshold at which special upgrade pricing becomes optimal. Finally, we also analyze the impact of upgrade pricing on the total coverage of the market.     

Lot‐sizing Decisions Under Limited‐time Price Reduction*

We consider the optimal lot‐sizing policy for an inventoried item when the vendor offers a limited‐time price reduction. We use the discounted cash flow (DCF) approach in our analysis, thereby eliminating the sources of approximation found in most of the earlier studies that use an average annual cost approach. We first characterize the optimal lot‐sizing policies and their properties, then develop an algorithm for determining the optimal lot sizes. We analytically demonstrate that the lot sizes derived using an average annual cost approach for the different variants of the problem are, in general, larger than the DCF optimum. While DCF analysis is more rigorous and yields precise lot sizes, we recognize that the associated mathematical models and the solution procedure are rather complex. Since simple and easy‐to‐understand policies have a strong practical appeal to decision makers, we propose a DCF version of a simple and easy‐to‐implement heuristic called the “Early Purchase” (EP) strategy and discuss its performance. We supplement our analytical developments with a detailed computational analysis and discuss the implications of our findings for decision making.     

A Comparison of Selected Artificial Neural Networks that Help Auditors Evaluate Client Financial Viability

This study compares the performance of three artificial neural network (ANN) approaches—backpropagalion, categorical learning, and probabilistic neural network—as classification tools to assist and support auditor's judgment about a client's continued financial viability into the future (going concern status). ANN performance is compared on the basis of overall error rates and estimated relative costs of misclassificaticn (incorrectly classifying an insolvent firm as solvent versus classifying a solvent firm as insolvent). When only the overall error rate is considered, the probabilistic neural network is the most reliable in classification, followed by backpropagation and categorical learning network. When the estimated relative costs of misclassification are considered, the categorical learning network is the least costly, followed by backpropagation and probabilistic neural network.     

A FUZZY SUBSET CHARACTERIZATION OF A SITE-SELECTION PROBLEM

This paper presents an application of fuzzy subsets to the problem of selecting sites for locating gas stations. Cost minimization and profit maximization models represent typical approaches to location decisions. These approaches are not well-suited to problems like site selection for gas stations because of the difficulty in identifying precise relationships, the uncertainties involved, and the qualitative and imprecise nature of much of the information used for decision making. This paper proposes the use of a model based on linguistic variables and a direct evaluation of the available alternatives via the use of fuzzy variables. Conclusions regarding the practical advantages and limitations of the approach are drawn based on the reported application.