Merger policy in innovative industries

We analyze optimal merger policy in R&D-intensive industries with product innovation aiming to improve the quality of products. Our results suggest that a permissive merger policy is rarely optimal in high-tech industries when the antitrust authority considers a welfare standard that balances the impact of mergers on consumers’ surplus and firms’ profits. In particular, relative to a benchmark where the effects from R&D are absent, we show that the optimal merger policy should not be substantially more permissive in the presence of those effects from R&D.     

Community-Based Tradable Permits for Localized Pollution

This paper considers the assignment of tradable permits—representing property rights of an environmental good—to community members who are harmed by pollution generated by firms. These community members can in turn sell permits to polluters according to their personal preferences. For a special case with a sole household, market transactions between the household and polluters achieve an efficient pollution level. However, for a group of households, the decentralized market solution fails to yield social efficiency because of competitive consumption of the environmental goods. We design a revenue-sharing mechanism akin to unitization, under which market transactions also achieve efficient resource allocation. Importantly, in some cases, efficiency can be achieved even when regulators are ignorant of the private valuation of the environmental good.     

The marketing of love: how attachment styles affect romantic consumption journeys

Journal of the Academy of Marketing Science - The experience of romantic love is closely interlocked with consumption journeys—yet how and why consumers engage in romantic consumption is not...     

BidPacket: trading bandwidth in public spaces

Smart devices such as smartphones and tablets are used extensively in public spaces for the transmission and reception of content in the form of text, photos and streaming videos. Since the bandwidth provided for wireless access is limited in public areas, it becomes an issue for users to gain access to the bandwidth they need at the right times. While an omniscient controller could assign bandwidth to each device on the basis of their needs and overall availability, imperfect information about the instantaneous state of the wifi access patterns and needs of users make for a very inefficient allocation of such bandwidth. This paper provides a solution for bandwidth allocation by creating a market among users of smart devices so that they can bid for extra bandwidth when they need it and sell it when they don’t. They do so by using a virtual currency that is conserved so that each device owner maximizes his own utility. This utility function is composed of both the benefit accrued from accessing bandwidth and the loss of the currency incurred in bidding for such bandwidth. Extensive simulations show that this market-based method outperforms an omniscient model when demand is uncertain, while minimizing bandwidth consumption.     

Assessment of Russian aviation development for long-term prospects with the regard for Arctic developing: reproductive and technological aspects

The article considers the conditions of developing the aviation industry for long-term prospects taking into account factors such as changes in purchasing aviation equipment, progress in developing the Russian aviation market, and works for reconstructing and modernizing transport infrastructure in the Russian Arctic. The author aims to coordinate the Government programs for developing new aviation equipment and equipment for air transport via the program of Arctic Exploration and Development, as it is necessary to integrate various cycle flows with regard to high-tech capital.     

Making sense of complex data using interactive data visualization

An important role for accountants today is to provide decision support to senior management by assisting them in the analysis of large, complex data sets. Interactive data visualization (IDV) facilitates this process by allowing users to navigate, select, and display data via an easy-to-use interface often used as a component of data analytics. Given the increasing popularity of IDV as a tool for making sense of complex data, it is important that accountants become familiar with and learn how to use this technology. This case provides a hands-on opportunity to organize complex accounting data to create IDVs for decision makers to use. Further, the case enables students to understand the potential impact of IDVs on preparers and users of accounting information. Students will assume the role of a division controller in a hypothetical company and create an IDV to assist the chief executive officer (CEO) in decision making.     

The Relative Performance of Poisson and Negative Binomial Regression Estimators

Negative binomial estimators are commonly used in estimating models with count‐data dependent variables. In this paper, sampling experiments are used to evaluate the performance of these estimators relative to the simpler Poisson estimator in finite‐sample situations. The results do not suggest a clear preference for negative binomial estimators in situations in which the underlying dependent variables are overdispersed, unless the researcher is comfortable in assumptions about the precise form of the overdispersion.     

Two-stage DEA: <Emphasis Type="Italic">caveat emptor</Emphasis>

This paper examines the wide-spread practice where data envelopment analysis (DEA) efficiency estimates are regressed on some environmental variables in a second-stage analysis. In the literature, only two statistical models have been proposed in which second-stage regressions are well-defined and meaningful. In the model considered by Simar and Wilson (J Prod Anal 13:49–78, 2007), truncated regression provides consistent estimation in the second stage, where as in the model proposed by Banker and Natarajan (Oper Res 56: 48–58, 2008a), ordinary least squares (OLS) provides consistent estimation. This paper examines, compares, and contrasts the very different assumptions underlying these two models, and makes clear that second-stage OLS estimation is consistent only under very peculiar and unusual assumptions on the data-generating process that limit its applicability. In addition, we show that in either case, bootstrap methods provide the only feasible means for inference in the second stage. We also comment on ad hoc specifications of second-stage regression equations that ignore the part of the data-generating process that yields data used to obtain the initial DEA estimates.