CRIME AND POVERTY: A SEARCH‐THEORETIC APPROACH*

Numerous studies document that criminal activity is positively related to unemployment and negatively related to educational attainment levels within given communities. We study this phenomenon in the context of a search‐equilibrium model, in which agents choose between formal employment and pursuing crime‐related activities (theft). Prior to their “occupational choices,” agents undertake costly schooling, raising their productivity. Crime acts, in essence, as a tax on human capital by affecting the probability that a worker's earnings (possessions) are subsequently appropriated. There are multiple equilibria. High crime, low levels of educational attainment, long spells of unemployment, and poverty are correlated across them.     

The Contingent Value of Responsive and Proactive Market Orientations for New Product Program Performance*

While the benefits of being market oriented are largely accepted, a group of scholars and managers remain skeptical. Marketing scholars have sought to counter the criticisms leveled against market orientation (MO) by arguing that it has both responsive and proactive dimensions. However, few studies have empirically examined the complexity of the effects of these dimensions on firm performance. Drawing on theories of resource‐based advantage and organizational search behavior, this article advances understanding by arguing that responsive and proactive market orientations have curvilinear effects on product development performance, that their interaction may be positively related to product development performance, and that their effects on new product program performance are moderated differentially by the organizational implementation conditions and marketing function power. Survey results of 175 U.S. firms indicate support for most of the hypotheses. Specifically, whereas responsive MO has a U‐shaped relationship with new product program performance, proactive MO has an inverted U‐shaped relationship with new product program performance. Contrary to the arguments presented here, the interaction of both orientations is negatively related to new product program performance. This study finds that both orientations are needed; however, new product program performance is enhanced when one is at higher level and the other is at lower level. Finally, responsive MO is only positively related to new product program performance under specific conditions such as when strategic consensus among managers is high. On the other hand, the positive effect of proactive MO on new product program performance is further strengthened when learning orientation and marketing power are high. Overall, this study suggests that the effects of responsive and proactive MO on new product program performance are more complex than previously theoretically argued and empirically examined.     

Feature selection methods involving support vector machines for prediction of insolvency in non‐life insurance companies

We propose two novel approaches for feature selection and ranking tasks based on simulated annealing (SA) and Walsh analysis, which use a support vector machine as an underlying classifier. These approaches are inspired by one of the key problems in the insurance sector: predicting the insolvency of a non‐life insurance company. This prediction is based on accounting ratios, which measure the health of the companies. The approaches proposed provide a set of ratios (the SA approach) and a ranking of the ratios (the Walsh analysis ranking) that would allow a decision about the financial state of each company studied. The proposed feature selection methods are applied to the prediction the insolvency of several Spanish non‐life insurance companies, yielding state‐of‐the‐art results in the tests performed. Copyright © 2005 John Wiley & Sons, Ltd.     

Artificial neural networks with feature transformation based on domain knowledge for the prediction of stock index futures

A feature transformation method based on domain knowledge for arti?cial neural networks (ANNs) is proposed. The method of feature transformation based on domain knowledge converts continuous values into discrete values in accordance with the knowledge of experts in speci?c application domains. This approach effectively ?lters data, trains the classi?er, and extracts the rules from the classi?er. In addition, it reduces the dimensionality of the feature space, which not only decreases the cost and time in the operation but also enhances the generalizability of the classi?er. The experimental results of the proposed approach will be compared and tested statistically with the results of the linear transformation method. The results show that the method of feature transformation based on domain knowledge outperforms the linear transformation in modelling of ANNs. Copyright © 2004 John Wiley & Sons, Ltd.     

Analysing Perceived Downside Risk: the Component Value‐at‐Risk Framework

Multinational companies face increasing risks arising from external risk factors, e.g. exchange rates, interest rates and commodity prices, which they have learned to hedge using derivatives. However, despite increasing disclosure requirements, a firm's net risk profile may not be transparent to shareholders. We develop the ‘Component Value‐at‐Risk (VaR)’ framework for companies to identify the multi‐dimensional downside risk profile as perceived by shareholders. This framework allows for decomposing downside risk into components that are attributable to each of the underlying risk factors. The firm can compare this perceived VaR, including its composition and dynamics, to an internal VaR based on net exposures as it is known to the company. Any differences may lead to surprises at times of earnings announcements and thus constitute a litigation threat to the firm. It may reduce this information asymmetry through targeted communication efforts.     

Consumer value conflicts surrounding ethical food purchase decisions: a focus on animal welfare

Ethical attitudes in relation to meat purchases were studied among urban and rural consumers in Scotland. All subjects perceived at least some ethical issues in relation to animal production systems, in particular, systems keeping animals in close confinement. Welfare‐friendly production systems were viewed as adding value to a food, but this value was not necessarily realizable to producers if purchases occurred only when foods were on special offer. Statements made by individuals were often contradictory, revealing ambivalence, unresolved value conflicts and a general lack of involvement in the nature of meat production. A number of barriers to the establishment of stable attitudes and behaviours in relation to the ethical treatment of food animals were also identified. A key finding of the study is that individuals can hold two views on animal welfare. On the one hand, they may think as citizens influencing societal standards, and on the other, as consumers at the point of purchase. As citizens, they support the notion of animals being entitled to a good life; as meat consumers, they avoid the cognitive connection with the live animal. This paper explores both the citizen–consumer relationship and purchase strategies used by consumers to resolve value conflicts. Lessons for public and commercial policy are highlighted in the context of the Curry Report (2002) which advocates more effective market segmentation where markets are finely attuned to their customers, with the development of a number of assurance schemes discussed in the article.     

The service sector of Cuba: An analysis and critique

The Cuban economy has experienced a significant increase in foreign direct investment over the past decade due to the end of the Soviet Union and the establishment of more liberal trade policies. Although the U.S. embargo of trade with Cuba still exists, there has been movement lately that suggests the end of the four‐decades‐old policy. The purpose of this article is to analyze the current Cuban business environment and to identify potential entrepreneurial opportunities in the service sector. Major trade partners are also reviewed and managerial implications discussed. © 2006 Wiley Periodicals, Inc.     

Statistical Decision Problems and Bayesian Nonparametric Methods

This paper considers parametric statistical decision problems conducted within a Bayesian nonparametric context. Our work was motivated by the realisation that typical parametric model selection procedures are essentially incoherent. We argue that one solution to this problem is to use a flexible enough model in the first place, a model that will not be checked no matter what data arrive. Ideally, one would use a nonparametric model to describe all the uncertainty about the density function generating the data. However, parametric models are the preferred choice for many statisticians, despite the incoherence involved in model checking, incoherence that is quite often ignored for pragmatic reasons. In this paper we show how coherent parametric inference can be carried out via decision theory and Bayesian nonparametrics. None of the ingredients discussed here are new, but our main point only becomes evident when one sees all priors—even parametric ones—as measures on sets of densities as opposed to measures on finite-dimensional parameter spaces.