Optimal Three-Part Tariff Plans

Service providers, such as cell phone carriers, often offer three-part tariff plans that consist of three levers: A fixed fee, an allowance of free units, and a price per unit above the allowance. In previous studies the optimal three-part tariff contract was characterized using the standard first-order conditions approach. Because this optimization problem is nonsmooth, however, it could only be solved in a few simple cases. In this study we employ a different methodology that is based on obtaining a global bound for the firm profit, and then showing that this bound is attained by the optimal plan. This approach allows us to explicitly calculate the optimal three-part tariff plan under quite general conditions, where consumers are rational, they have a general utility function, they experience psychological costs when they exceed the number of free units, they have deterministic or stochastic consumption rates, they are homogeneous or heterogeneous, and the firm costs are fixed or depend on the usage level

Regulating a Monopoly with Universal Service Obligation: The Role of Flexible Tariff Schemes

This paper’s purpose is to study the problem of a regulator of a utility monopoly, who has a universal service goal that is binding, in the sense that there is no two- part tariff that can induce efficient consumption, self-finance the firm, and guarantee universal access at the same time. The optimal two-part tariffs that the regulator should set under the following three different regulatory rules are derived: no flexibility (the monopolist just offers the regulated plan), partial flexibility (the monopolist can offer alternative plans, but these -and the regulated one- must be available to all customers), and full flexibility (the regulated plan must be available to all customers, but not the alternative ones). The solutions under the three schemes are characterized, and provide an unambiguous ranking of regulatory rules: total flexibility is weakly better than partial flexibility, with the latter being strictly better than no flexibility.Monopoly regulation, network utilities, universal service obligation, Non-Linear Tariffs

Why Tax Energy? Towards a More Rational Energy Policy

The same fuels are taxed at widely different rates in different countries while different fuels are taxed at widely different rates within and across countries. Coal, oil and gas are all used to generate electricity, but are subject to very different tax or subsidy regimes. This paper considers what tax theory has to say about efficient energy tax design. The main factors for energy taxes are the optimal tariff argument, the need to correct externalities such as global warming, and second-best considerations for taxing transport fuels as road charges, but these are inadequate to explain current energy taxes. EU energy tax harmonisation and Kyoto suggest that the time is ripe to reform energy taxation

Uncertain Demand, Consumer Loss Aversion, and Flat-Rate Tariffs

The so called flat-rate bias is a well documented phenomenon caused by consumers' desire to be insured against fluctuations in their billing amounts. This paper shows that expectation-based loss aversion provides a formal explanation for this bias. We solve for the optimal two-part tariff when contracting with loss-averse consumers who are uncertain about their demand. The optimal tariff is a flat rate if marginal cost of production is low compared to a consumer's degree of loss aversion and if there is enough variation in the consumer's demand. Moreover, if consumers differ with respect to the degree of loss aversion, firms' optimal menu of tariffs typically comprises a flat-rate contract

Analysis of the Effectiveness of Tariffs for Telecommunications Services with Broadband Access

The purpose of the study was to analyze the efficiency of tariffs of companies providing telecommunications services with broadband access. The analysis was carried out with respect to both the efficiency of the tariff system in general and its individual elements. The structure of costs for services providing access to the Internet and networks of digital television has been studied. The scheme of the analysis of the product and cost parts of the tariff system of telecommunication companies is proposed. The advantages and disadvantages of pricing strategies for telecommunication services are discussed

Analysis of cloud storage prices

Cloud storage is fast securing its role as a major repository for both consumers and business customers. Many companies now offer storage solutions, sometimes for free for limited amounts of capacity. We have surveyed the pricing plans of a selection of major cloud providers and compared them using the unit price as the means of comparison. All the providers, excepting Amazon, adopt a bundling pricing scheme; Amazon follows instead a block-declining pricing policy. We compare the pricing plans through a double approach: a pointwise comparison for each value of capacity, and an overall comparison using a two-part tariff approximation and a Pareto-dominance criterion. Under both approaches, most providers appear to offer pricing plans that are more expensive and can be excluded from a procurement selection in favour of a limited number of dominant providers.Comment: 17 pages, 17 figures, 17 reference

Boosting insights in insurance tariff plans with tree-based machine learning methods

Pricing actuaries typically operate within the framework of generalized linear models (GLMs). With the upswing of data analytics, our study puts focus on machine learning methods to develop full tariff plans built from both the frequency and severity of claims. We adapt the loss functions used in the algorithms such that the specific characteristics of insurance data are carefully incorporated: highly unbalanced count data with excess zeros and varying exposure on the frequency side combined with scarce, but potentially long-tailed data on the severity side. A key requirement is the need for transparent and interpretable pricing models which are easily explainable to all stakeholders. We therefore focus on machine learning with decision trees: starting from simple regression trees, we work towards more advanced ensembles such as random forests and boosted trees. We show how to choose the optimal tuning parameters for these models in an elaborate cross-validation scheme, we present visualization tools to obtain insights from the resulting models and the economic value of these new modeling approaches is evaluated. Boosted trees outperform the classical GLMs, allowing the insurer to form profitable portfolios and to guard against potential adverse risk selection