Political pressures and the credibility of regulation: can profit sharing mitigate regulatory risk?

When price-cap rules determine the structure of prices for a long period, they suffer a credibility problem and introduce an element of risk especially if a firm’s profits are “too large”. Profit sharing may be seen as a device to pre-determine price adjustments and thus to decrease regulatory risk. We analyse the effects of profit sharing on the incentives to invest, using a real option approach. Absent credibility issues, a well designed profit sharing system may be neutral relative to a pure price cap. With regulatory risk, profit sharing is preferable to a pure price-cap one, if it intervenes for high enough profit levels.

An Approach to Security for Unstructured Big Data

Security of Big Data is a huge concern. In a broad sense, Big Data contains two types of data: structured and unstructured. Providing security to unstructured data is more difficult than providing security to structured data. In this paper, we have developed an approach to provide adequate security to unstructured data by considering types of data and their sensitivity levels. We have reviewed the different analytics methods of Big Data to build nodes of different types of data. Each type of data has been classified to provide adequate security and enhance the overhead of the security system. To provide security to a data node, and a security suite has been designed by incorporating different security algorithms. Those security algorithms collectively form a security suite which has been interfaced with the data node. Information on data sensitivity has been collected through a survey. We have shown through several experiments on multiple computer systems with varied configurations that data classification with respect to sensitivity levels enhances the performance of the system. The experimental results show how and in what amount the designed security suite reduces overhead and increases security simultaneously.     

Pollution Abatement Subsidies and the Eco-Industry

This paper considers the combination of pollution taxes and abatement subsidies when some polluting firms procure their abatement goods and services from an oligopolistic eco-industry. The regulator must here cope with two simultaneous price distortions: one that comes from pollution and the other which is caused by the eco-industry’s market power. In this context, we show that taxing emissions while subsidizing polluters’ abatement efforts cannot lead to first-best, but the opposite occurs provided it is the eco-industry’s output which is subsidized. When public transfers also create distortions, welfare can be higher if the regulator uses only an emission tax, but subsidizing abatement suppliers while taxing emissions remains optimal when the eco-industry is concentrated.     

Hybrid value creation: a systematic review of an evolving research area

This systematic review analyses literature on the work of hybrid value creation, i.e. the process of generating additional value by innovatively combining products (tangible component) and services (intangible component). A state of the art report on hybrid value creation is delivered by first systematically identifying and then analyzing 169 publications focusing on hybrid value creation. The identified publications are clustered into eight categories based on their links and interactions and thus a mapping of this evolving field is suggested. A discussion and reflection of the findings with respect to the pervasiveness of literature and the research methodologies used is provided. The paper concludes by identifying some dominant strategic gaps in the overall research landscape and provides directions for future research.     

Sequential control of time series by functionals of kernal-weighted empirical processes under local alternatives

Motivated in part by applications in model selection in statistical genetics and sequential monitoring of financial data, we study an empirical process framework for a class of stopping rules which rely on kernel-weighted averages of past data. We are interested in the asymptotic distribution for time series data and an analysis of the joint influence of the smoothing policy and the alternative defining the deviation from the null model (in-control state). We employ a certain type of local alternative which provides meaningful insights. Our results hold true for short memory processes which satisfy a weak mixing condition. By relying on an empirical process framework we obtain both asymptotic laws for the classical fixed sample design and the sequential monitoring design. As a by-product we establish the asymptotic distribution of the Nadaraya-Watson kernel smoother when the regressors do not get dense as the sample size increases.Acknowledgements The author is grateful to two anonymous referees for their constructive comments, which improved the paper. One referee draws my attention to Lifshits paper. The financial support of the Collaborative Research Centre Reduction of Complexity in Multivariate Data Structures (SFB 475) of the German Research Foundation (DFG) is greatly acknowledged.