Culture and the demand for non-life insurance: Empirical evidences from middle-income and high-income economies*

This paper investigates the impact of culture on non-life insurance expenditure using a panel dataset covering 27 middle-income countries and 40 high-income countries in the period 1980–2014. Based on the two-step system GMM estimator, it is found that several cultural dimensions such as masculinity, uncertainty avoidance and power distance significantly explain the spending on non-life insurance in middle-income countries, while masculinity is the only cultural factor that significantly explains spending on non-life insurance in high-income countries. Traditional drivers of the demand for non-life insurance, such as per capita income, economic freedom and urbanization, also behave differently comparing between the two income-based groups of countries. These findings provide useful recommendations for business firms and policymakers around the world, especially transitional economies, to promote and sustain the development of the non-life insurance sector, which is an important engine for growth and prosperity.     

Market structure and casino taxation in tourist resorts

This article examines the association of tax effects with market structure for casino gaming. We show that if market structure is uncompetitive, much of casino taxation falls on tourists whose demand is inelastic relative to supply. The tax is likely to be efficient under strong external demand if imposed on oligopoly casinos with a monopoly location in a cross-border market. The likelihood of economically ‘good’ taxation is greater under oligopoly than under competition but lower than under monopoly. Casino taxes should be lowered in a more competitive market with weaker external demand. Our prediction is consistent with the evidence found from casino tourism development in Macao with ‘high’ gambling taxes.     

Quantile models and estimators for data analysis

Quantile regression is used to estimate the cross sectional relationship between high school characteristics and student achievement as measured by ACT scores. The importance of school characteristics on student achievement has been traditionally framed in terms of the effect on the expected value. With quantile regression the impact of school characteristics is allowed to be different at the mean and quantiles of the conditional distribution. Like robust estimation, the quantile approach detects relationships missed by traditional data analysis. Robust estimates detect the influence of the bulk of the data, whereas quantile estimates detect the influence of co-variates on alternate parts of the conditional distribution. Since our design consists of multiple responses (individual student ACT scores) at fixed explanatory variables (school characteristics) the quantile model can be estimated by the usual regression quantiles, but additionally by a regression on the empirical quantile at each school. This is similar to least squares where the estimate based on the entire data is identical to weighted least squares on the school averages. Unlike least squares however, the regression through the quantiles produces a different estimate than the regression quantiles.     

Managing customer expectations in financial services: Opportunities and challenges

Although studies have been conducted to advance our understanding of the concept of expectations, a number of challenges have been created in relation to the nature, types and dynamics of these expectations, and the effects of expectations on subsequent evaluations, both of which need closer attention. The objectives of this paper are to examine these challenges in the context of financial services, and discuss how financial service providers can manage customer expectations to their advantage. A set of propositions is developed, and suggestions for future research are also discussed.     

The Usefulness of Derivative-related Accounting Disclosures

The Financial Accounting Standards Board attempts to improve reporting and disclosure of derivative transactions through SFAS Nos. 105, 107, and 119. These statements require recognition of gains or losses on trading purpose derivatives, and disclosure of notional principal amounts, credit exposures, and fair values of trading and nontrading derivatives. Using a multiple regression model, this study investigates the relevance of these disclosures to stock returns for a sample of large banks. All derivatives-related disclosures, except for notional principal amounts, are found to contain new information not incorporated in market beta and earnings. These results support the Board's derivative disclosure requirements.     

Tourism development and value change

Social changes induced by tourism development are examined from the standpoint of modernization and capitalist development in Vent and Obergurgl, two small Alpine communities in Western Austria. The level of development of these two villages regarding structure and values is determined empirically and the existence of a relationship between these two levels established: traditional precapitalist structures and values dominate in Vent, while modern capitalist structures and values prevail in Obergurgl. Analysis of the conditions and processes of change showed tourism to be acceptable to traditional precapitalist structures as well as to modern capitalist structures and values. However, once tourism evolves to a “mono-culture”, it transcends the traditional precapitalist framework.     

A Comparative Analysis of Inductive-Learning Algorithms

Recently there has been an increasing interest in applying inductive learning algorithms to generate rules/patterns from a given example set. While such approaches serve as an efficient way of resolving the knowledge-acquisition bottleneck, their predictive accuracy, which is the popular measure of performance, varies widely. This paper contrasts major inductive-learning algorithms and examines their performance with two performance measures: the predictive accuracy and the representation language. Experiments involved three inductive-learning algorithms and five different managerial tasks in construction project assessment and bankruptcy-prediction domains. The test results indicate that the model performance is dependent on tasks with an exception of the neural network model and that there is a an effect of group proportion in the example set used to construct the model. The neural network approach presents relatively stable predictive power across different task domains, although it is difficult to interpret its representation.