The incidence of unethical practices in marketing research: An empricial investigation

The authros examine from the perspective of marketing professionals the incidence of unethical research practices and the influence of organizational factors as determinants of the incidence of unethical research practices. The results indicate some degree of indicence of unethical research practices, particularly of those involving respondents. In addition, the results suggest four organizational variables— extent of ethical problems within the organization, top management actions on ethics, organizational role, and industry category—as determinants of the incidence of unethical research partices.     

Lost and found: Individual differences in propensity to process visual elements of persuasion

Visual processing style, defined as the relative propensity to engage in visual processing rather than verbal processing, is an individual difference variable that has been frequently investigated in the consumer psychology literature. Surprisingly, numerous studies have reported no relationship between visual processing style and viewer responses to visual elements of persuasion. We argue that this accumulation of null results is due to untenable historical theoretical assumptions that underlie the construct, along with methodological problems that are inevitably brought about by those theoretical assumptions. We reconceptualize visual processing style and test an alternative empirical operationalization of it. Using both new data and a reanalysis of data published in the Journal of Consumer Research, we find the old approach yields null results, but we find the new approach yields the expected results. The new approach reinstates the utility of incorporating propensity to engage in visual processing as an individual difference variable into consumer psychology models of visual persuasion, and it reinstates a powerful individual difference variable that can help push forward the investigation of the unique aspects of visual persuasion.     

Credibility Using Copulas

Abstract

Credibility is a form of insurance pricing that is widely used, particularly in North America. The theory of credibility has been called a “cornerstone” in the field of actuarial science. Students of the North American actuarial bodies also study loss distributions, the process of statistical inference of relating a set of data to a theoretical (loss) distribution. In this work, we develop a direct link between credibility and loss distributions through the notion of a copula, a tool for understanding relationships among multivariate outcomes.

This paper develops credibility using a longitudinal data framework. In a longitudinal data framework, one might encounter data from a cross section of risk classes (towns) with a history of insurance claims available for each risk class. For the marginal claims distributions, we use generalized linear models, an extension of linear regression that also encompasses Weibull and Gamma regressions. Copulas are used to model the dependencies over time; specifically, this paper is the first to propose using a t-copula in the context of generalized linear models. The t-copula is the copula associated with the multivariate t-distribution; like the univariate tdistributions, it seems especially suitable for empirical work. Moreover, we show that the t-copula gives rise to easily computable predictive distributions that we use to generate credibility predictors. Like Bayesian methods, our copula credibility prediction methods allow us to provide an entire distribution of predicted claims, not just a point prediction.

We present an illustrative example of Massachusetts automobile claims, and compare our new credibility estimates with those currently existing in the literature.     

Weighted Pricing Functionals With Applications to Insurance

Abstract

We explore the role of weighted distributions in pricing insurance risks. In particular, we relate the distributions to actuarial and economic premium calculation principles and in this way provide a unifying methodology for constructing new principles and analyzing known ones.     

Corporate strategy financialized: Conjuncture,arbitrage and earnings capacity in the S&P500

The conjuncture that ushered in the era of shareholder value served to embed capital market expectations into corporate governance aligning management and shareholder interests. Market arbitrage focussed on modifying contractual relations with stakeholders to extract a (higher) return on invested capital. In this article we focus on cash earnings on capital employed generated by the S&P 500 survivor group of firms covering the period 1990–2008. We use this financial data to construct three complementary perspectives on corporate financial performance: firm, firm-relative and macro. Within this framework the financial numbers and perspectives are analogous to a ‘hall of mirrors’ where ambiguity and contradiction are in play frustrating straightforward performative narratives that connect purpose with financial transformation an era of shareholder value.     

Perfectly competitive bilateral exchange without discounting

In a random-matching economy of traders who maximize cumulative consumption (overtaking criterion), the stationary, Markov, Bayesian-perfect equilibrium is studied. At such equilibrium, two results hold: (1) perfect substitutability between current and future consumption implies a no-surplus condition; and (2) by the no-surplus condition, there is a nominal price at which all trades must occur. These results strengthen the seminal results of Ostroy (1973) regarding monetary bilateral exchange in two ways: the incentive compatibility of the equilibrium trading pattern is established and a less roundabout trading pattern enhances welfare by enabling consumption to occur more frequently.     

全球化：前景、潜力与问题

本文扼要阐述了经济全球化发展的前景和潜力,并运用李嘉图模型对全球化可能导致的经济损害作出论证分析进而说明,尽管经济学家在极大程度上趋向于将全球化视为基本良性的现象,但全球化仍然对某些国家（包括发达国家）带来经济损害,及对部分人群可能造成极其痛苦的后果。不过,源自于全球化的不断增长的竞争压力,会加强创新和增长,从长远来看,的确对每个国家有利。     

On the Application of Markov Chain Monte Carlo Methods to Genetic Analyses on Complex Pedigrees

Markov chain Monte Carlo methods are frequently used in the analyses of genetic data on pedigrees for the estimation of probabilities and likelihoods which cannot be calculated by existing exact methods. In the case of discrete data, the underlying Markov chain may be reducible and care must be taken to ensure that reliable estimates are obtained. Potential reducibility thus has implications for the analysis of the mixed inheritance model, for example, where genetic variation is assumed to be due to one single locus of large effect and many loci each with a small effect. Similarly, reducibility arises in the detection of quantitative trait loci from incomplete discrete marker data. This paper aims to describe the estimation problem in terms of simple discrete genetic models and the single-site Gibbs sampler. Reducibility of the Gibbs sampler is discussed and some current methods for circumventing the problem outlined.