Do non-profit hospitals provide more charity care when faced with a mandatory minimum standard? Evidence from Texas

Policymakers often question whether not-for-profit (NFP) hospitals provide enough charity care to justify tax advantages. In 1993, Texas enacted legislation requiring NFP hospitals to provide certain community benefits at levels set forth in specific criteria to retain tax-exempt status; this paper focuses on the effect of the legislation’s requirement that NFP hospitals spend a minimum of 4% of net patient revenue on charity care. We also study a modification of the law passed in 1995, which allows the deduction of bad debts expense when calculating net patient revenue. This change effectively lowers required charity care spending, and our study considers whether Texas hospitals responded by reducing charity care spending.     

Predicting Corporate Failure Using a Neural Network Approach

This paper investigates the performance of Artificial Neural Networks for the classification and subsequent prediction of business entities into failed and non-failed classes. Two techniques, back-propagation and Optimal Estimation Theory (OET), are used to train the neural networks to predict bankruptcy filings. The data are drawn from Compustat data tapes representing a cross-section of industries. The results obtained with the neural networks are compared with other well-known bankruptcy prediction techniques such as discriminant analysis, probit and logit, as well as against benchmarks provided by directly applying the bankruptcy prediction models developed by Altman (1968) and Ohlson (1980) to our data set. We control the degree of ‘disproportionate sampling’ by creating ‘training’ and ‘testing’ populations with proportions of bankrupt firms ranging from 1% to 50%. For each population, we apply each technique 50 times to determine stable accuracy rates in terms of Type I, Type II and Total Error. We show that the performance of various classification techniques, in terms of their classification errors, depends on the proportions of bankrupt firms in the training and testing data sets, the variables used in the models, and assumptions about the relative costs of Type I and Type II errors. The neural network solutions do not achieve the ‘magical’ results that literature in this field often promises, although there are notable 'pockets' of superior performance by the neural networks, depending on particular combinations of proportions of bankrupt firms in training and testing data sets and assumptions about the relative costs of Type I and Type II errors. However, since we tested only one architecture for the neural network, it will be necessary to investigate potential improvements in neural network performance through systematic changes in neural network architecture.     

Modelling Seasonality in the Australian Consumption Function

This paper puts forward the proposition that failure to take account of stochastic seasonality results in dynamic misspecification. This point is illustrated by estimating various versions of a simple Australian consumpt ion function using seasonally unadjusted data covering the period 1959(3)–1993(3). It is demonstrated that only the equation modelling stochastic seasonality as an unobserved component shows no dynamic misspecification unlike other equations. This equation is also shown to have the highest explanatory power and, unlike other equations, to be structurally stable     

Moving towards consumer involvement in maternity services in the north‐east of Ireland

This article uses a case‐study approach to focus on developments in maternity services in the north‐east of Ireland where there is occurring a planned move towards active consumer involvement. In a country with a poor record of consumer involvement, this is a very welcome and innovative development. The Health Service in Ireland is in crisis, characterized by a very prolonged and difficult restructuring, extreme difficulties in recruiting and retraining staff, lack of resources, long waiting times in accident and emergency units with the media reporting daily counts of those left lying on trolleys for extended periods. The north‐east of the country has been affected by the closure of two maternity units in Monaghan and Dundalk. The region has been rocked in recent years by a major health scandal with a long‐serving obstetrician being struck off the medical register for carrying out an excessive number of caesarean hysterectomies. Out of this, there is emerging an innovative woman‐centred maternity service in the north‐east of the country. This article evaluates these developments with a particular focus on consumer involvement.     

Classical Swine Fever Changes the Way Farmers Value Pigs in South Africa

Although there is growing demand for animal products in Africa, production is stagnating. Appropriate management of livestock diversity could help reinvigorate production, contribute to food security and improve farmers’ livelihoods, particularly in subsistence‐oriented systems. We assess differences in farmers’ preferences and economic values for pig traits across different production systems and across areas that have been affected and unaffected by classical swine fever (CSF). Not surprisingly, market‐oriented farmers derived higher values from the productive traits such as heavy slaughter weight and large litter size found in exotic pig genotypes. Subsistence‐oriented farmers, particularly in swine fever affected areas, placed high value on tolerance to disease. We found that CSF changed farmers’ preferences for adaptive traits, and less so for productive traits. Therefore, indigenous breeds become more valuable for subsistence farmers and crossbreeds for market‐oriented farmers if CSF is a risk. Our results can have implications for breeding and conservation strategies and for compensation strategies after culling, and will become increasingly relevant if, as predicted, heat waves and disease outbreaks become more frequent in pig production systems in South Africa with climate change.     

Task‐Relationship‐Self: A Framework for Understanding Service Encounter Behaviors

Research conducted over past decades has investigated selected service encounter behaviors from either a customer or service provider perspective. However, a comprehensive, dual‐perspective framework is lacking. Such a framework is needed to organize knowledge of these behaviors, and thereby provide structure, clarity, and parsimony to the field. This paper describes a three‐tier framework of service encounter behavior that was developed by applying grounded theory principles to interviews with customers, service employees, and other stakeholders. These informants described many ways in which they behave when executing service exchanges, dealing with service difficulties, and managing themselves in the process. Using an iterative inductive approach, a conceptual framework was developed in which specific (Tier 1) behaviors were placed within broader (Tier 2) categories, and these lower classification levels were, in turn, interpreted within a conceptual space defined by the (Tier 3) dimensions of task, relationship, and self. This framework was then elaborated and refined by reference to the psychology and marketing literature, a set of 157 audio‐recorded service interactions, and an expert panel study. The paper includes comparisons between the framework and those previously proposed, propositions regarding service encounter processes and outcomes, and implications for future research and practice.