Pricing practices and firms' market power in international cellular markets, an empirical study

Previous studies on international marketing have typically asked the question: “how is the demand characterized across countries?” Such analysis is then used to provide guidelines for firms to enter new markets and/or to allocate marketing resources across countries. To provide such normative guidelines, however, one also needs to analyze the supply-side of the problem, i.e., ask: “what is the likely market power that firms will be able to command in different countries?” Building on the New Empirical Industrial Organization (NEIO) framework, recent research in marketing provides marketers with a variety of models to explore competitive interactions among firms in the context of a single market. The goal of this paper is to extend this literature to a multimarket/multinational context to help international marketers assess the likely market power they face when entering new countries. We illustrate the proposed method on the mobile telecommunications industry, using price and quantity data from 10 countries around the world, estimating firms' market power as a function of a number of country characteristics.The results indicate that, while the simple presence of competition diminishes firms' market power, it does not lead to perfect competition. Interestingly, a higher number of competitors in a country does not seem to have significant incremental effect on market power. In contrast, the country's commitment to a severe antitrust policy has a significant negative effect, while the monopolist's lead-time before competition is allowed has a significant positive effect on market power. These findings, together with a change in price elasticities as a result of competition, suggest that market power in different countries may originate from two sources: (i) collusive pricing among cellular operators and (ii) consumers' switching costs across service providers. For international marketers, the findings imply that the attractiveness of wealthier countries (with usually faster diffusion rates and larger market potential) may be mitigated by higher levels of competition (as a result of developed antitrust regulation and more consumer exposure to competitive marketing practices). From a policy point of view, it suggests that (in contrast to the conventional wisdom) simple deregulation may not be enough to reduce prices to competitive levels. In addition, a severe antitrust policy is crucial to achieve this goal.     

汇率波动模型与人民币汇率调整新趋势

文章对四种汇率波动模型进行简要介绍与说明,指出中国渐进式的改革和开放决定了入世后人民币汇率的制度调整也应是渐进式的,现阶段人民币汇率制度的选择应实行目标区汇率制。     

房地产股权投资信托当事人的利益均衡研究

简要介绍了房地产信托,对房地产股权投资型信托的委托人、受托人利受益人三方当事人的利益均衡进行分析讨论,建立了相应的数学模型,并通过将持股比例引入模型,使三方构成一个有机的整体,然后对模型进行比较详尽的分析研究,找出影响最优持股比例的因素并分析,得出一些有意义的结论。     

Economics and underdetermination: a case study of urban land and housing economics

Underdetermination, associated with the Duhem–Quine thesis,is a familiar if under-researched theme in economics. In thelight of this, we examine the development of urban land andhousing economics. Through its Cartesian dualistic delineationof theory and data, the contemporary mainstream approach appearsunable to circumvent the problem of underdetermination. In effect,it employs the strong version of Duhem–Quine in its retentionof the assumption of a single, unitary competitive market (andassociated access–space trade-off). Conversely, we highlightthe affinity of Ely's (and the later Columbia School's) approachto pragmatists Dewey and Peirce, which provides a more fruitfulbasis for explanation.     

反倾销应诉的集体行动研究——行业协会克服"搭便车"机制的思考

本文针时我国企业在反倾销共同应诉中的不合作或者“搭使车”问题，试图探讨行业协会作为一种治理机制，如何提供或设计一种有效的制度安排（如俱乐部规范），把一群相互依存的企业组织起来，并通过自主性努力克服不合作或“搭便车”，以取得长期的共同利益的实现。     

标准化在油藏监测工作中的作用

以油藏监测工作中的油藏压力监测为例，介绍了标准化工作在压力资料录取及解释分析中的作用。并用实例说明了标准化作业在油田开发方案的制定及油藏压力监测中的应用。     

Predicting the geo-temporal variations of crime and disorder

Traditional police boundaries—precincts, patrol districts, etc.—often fail to reflect the true distribution of criminal activity and thus do little to assist in the optimal allocation of police resources. This paper introduces methods for crime incident forecasting by focusing upon geographical areas of concern that transcend traditional policing boundaries. The computerised procedure utilises a geographical crime incidence-scanning algorithm to identify clusters with relatively high levels of crime (hot spots). These clusters provide sufficient data for training artificial neural networks (ANNs) capable of modelling trends within them. The approach to ANN specification and estimation is enhanced by application of a novel and noteworthy approach, the Gamma test (GT).     

Estimating the Size of a Criminal Population from Police Records Using the Truncated Poisson Regression Model

The truncated Poisson regression model is used to arrive at point and interval estimates of the size of two offender populations, i.e. drunk drivers and persons who illegally possess firearms. The dependent capture–recapture variables are constructed from Dutch police records and are counts of individual arrests for both violations. The population size estimates are derived assuming that each count is a realization of a Poisson distribution, and that the Poisson parameters are related to covariates through the truncated Poisson regression model. These assumptions are discussed in detail, and the tenability of the second assumption is assessed by evaluating the marginal residuals and performing tests on overdispersion. For the firearms example, the second assumption seems to hold well, but for the drunk drivers example there is some overdispersion. It is concluded that the method is useful, provided it is used with care.     

Analysing Materials and Energy Flows in an Industrial District using an Enterprise Input–Output Model

Recent approaches to sustainable development leave much room for policies at a local level. In fact, it is becoming evident that targets such as increasing resource productivity, preserving natural cycles, or extending the present level of welfare, are best pursued within the confines of a local area. In particular, environmental changes are best brought about by considering local systems of firms as cornerstones of cooperative strategies and using data on materials and energy use in physical terms. In this paper, an enterprise input–output model is developed for an industrial district, i.e. a local group of firms specialized in the production of a single final output. The model allows for a detailed quantitative analysis of materials and energy flows and the consequent generation of waste and pollution. As a planning tool, the model may be used to evaluate alternative scenarios, such as the possibility of re- using waste taking account of sustainability requirements. An empirical case study applies the model to an industrial district in Southern Italy producing leather sofas.     

Inferential Causal Models: Integrating Quality & Quantity

In-depth data analysis plus statistical modeling can produce inferentialcausal models. Their creation thus combines aspects of analysis by close inspection,that is, reason analysis and cross-tabular analysis, with statistical analysis procedures,especially those that are special cases of the generalized linear model (McCullaghand Nelder, 1989; Agresti, 1996; Lindsey, 1997). This paper explores some of the roots of this combined method and suggests some new directions. An exercise clarifies some limitations of classic reason analysis by showing how the cross tabulation of variables with controls for test factors may produce better inferences. Then, given the cross tabulation of several variables, by explicating Coleman effect parameters, logistic regressions, and Poisson log-linear models, it shows how generalized linear models provide appropriate measures of effects and tests of statistical significance. Finally, to address a weakness of reason analysis, a case-control design is proposed and an example is developed.