Tariff Contract Research Articles

Mergers and Licensing With Horizontal Differentiation

ABSTRACTWe consider a research laboratory that owns a patented process innovation and two firms producing differentiating goods in a Bertrand setting. The laboratory considers the possibility to license the innovation as an outsider patentee or to merge with one of the firms in the industry, becoming an incumbent patentee. Licensing takes place through observable two‐part tariff contracts. We show that the merger is profitable only for small innovations and increases social welfare for both small and large innovations. Even though we allow the royalty to be higher than the size of the innovation, and opposite to the result in a Cournot setting, we find a region where the merger is both profitable and welfare improving. This occurs only for small innovations and sufficiently differentiated goods. The same result arises for consumer surplus which allows us to derive the optimal merger policy: compared with Cournot competition, a Bertrand setting calls for a more lenient merger policy.

Effect of risk aversion on the performance of supply chain and carbon reducing initiatives under asymmetric information

AbstractThis paper considers a supply chain in which the risk‐averse manufacturer invests in carbon emission reduction (CER) technology and the risk‐averse retailer makes green sales efforts to reduce carbon footprint. The results show that when the information is symmetrical, the two‐part tariff contract can eliminate the double‐marginalization effect. Moreover, when the information is asymmetric, the manufacturer encourages retailer to report true market size and improve green sales effort by providing an incentive contract. This menu of contract ensures the manufacturer obtains more utilities with low carbon emissions. In addition, through the analysis of risk aversion coefficients, we find that under centralized decision, with the improvement of risk aversion awareness, both supply chain players will reduce the green efforts. However, under asymmetric information, compared with the manufacturer, the retailer's higher risk aversion will have an opposite effect on the equilibrium decision‐making, utilities, and carbon footprint of the supply chain.

Optimal patent licensing—Two or three‐part tariff

AbstractWe look into technology transfer by an insider patentee in a spatial duopoly model under three types of licensing contracts—(i) two‐part tariff with fixed fee and per‐unit royalty, (ii) two‐part tariff with fixed fee and ad‐valorem royalty and (iii) general three‐part tariff with fixed fee, per‐unit and ad‐valorem royalties. Under two‐part tariff contracts, the licenser is better off with the per‐unit royalty contract but the general contract does better than the other contracts. In contrast to the existing literature, all three licensing contracts may make the consumers worse‐off compared to no licensing, with the lowest consumer surplus achieved under the general licensing contract. Welfare under the general licensing contract is equal to the welfare under two‐part tariff with ad‐valorem royalty and it is higher than the welfare under no licensing but lower than the welfare under two‐part tariff with per‐unit royalty. Hence, the general three‐part licensing contract is privately optimal but not socially optimal. Similar conclusions hold also under a nonspatial linear demand model with differentiated products.

Managing supply chain with green and non‐green products: Channel coordination and information asymmetry

AbstractWe examine a supply chain where a supplier produces partial substitutable green and non‐green products and sell them in a common market through a buyer. We analyse four scenarios: wholesale price and linear two‐part tariff contracts under full and incomplete information about the buyer's unit cost. Our result demonstrates that the value of information and trade cutoff points both increase with consumer greening sensitivity and decrease with greening investment. When green product market share exceeds a certain threshold, demand for the green product would be higher than non‐green product, even when green product is priced higher in the end market.

Analyzing coordination strategy of circular supply chain in re‐commerce industry: A game theoretic approach

AbstractThe circular economy seeks to reduce waste by reusing, refurbishing, and recycling used products. It increases the resource value of used goods. In this study, we proposed a circular business gap exploiter model in which a re‐commerce company exploits residual value in product systems utilization. A game‐theoretic approach is used to investigate the coordination problem of a two‐echelon circular supply chain with e‐commerce and re‐commerce companies. In addition, we proposed a coordinated structure under the revenue sharing contract and the two‐part tariff contract through Nash negotiation to address the coordination problems. We compared the centralized and decentralized model findings and observed that the decentralized model shows potential for improvement. We showed model's applicability through numerical examples and sensitivity analysis to indicate how parameters such as wholesale price and retail price affect channel decisions and member profits. The results show that a two‐part tariff contract may efficiently coordinate the circular supply chain. The study shows that under a circular business model, two tactics may be applied. These are extending product value and extending resource value.

Marketplace or reselling: the pricing decisions and face value of the coupons under the Cap‐and‐Trade regulation

AbstractThis paper considers a manufacturer selling his products with the marketplace or the reselling mode under the cap‐and‐trade regulation. Coupons are allocated when selling the products. We explore the optimal decisions, the selection and the coordination with the two modes. First, we analytically find that the increase of the cap and platform power (commission rate) increases (decreases) the total production quantity and the optimal production quantity for coupon‐sensitive consumers, and interestingly find that it reduces (increases) the production quantity for coupon‐insensitive consumers when the sensitivity of the coupons is high. Furthermore, the optimal profit of the manufacturer first increases and then decreases with the cap when the emission intensity is low, and it increases with the cap when the emission intensity is high. Second, the marketplace mode creates more (less) profits for the manufacturer if the order‐fulfillment cost is low (high). Whether the marketplace mode or the reselling mode will generate more carbon emission depends on the commission rate and the platform power. Finally, the marketplace mode is not capable of coordinating the manufacturer and the platform at all times, while the reselling mode can coordinate the two firms after considering a coupon‐sharing contract under considerable platform power, and they can achieve Pareto improvement with a two‐part tariff contract under the generalized Nash bargaining framework.

Coordination of New Energy Vehicles Closed‐Loop Supply Chain under Government Subsidies and Different Power Structures

With the gradual retirement of the first batch of new energy vehicles in recent years, determining the optimal recycling mode has become an urgent concern. Considering the closed‐loop supply chain, the government subsidy system, and different market power structures, this paper studies new energy vehicle recycling decisions and supply chain contract coordination. The results show that given an increase in government subsidies, the profit of the dominant agent in the closed‐loop supply chain will be higher than that of the follower, and an increase in wholesale and recovery prices may lead to an increase in sales prices. In addition, the effect of government subsidies on battery recovery is better in cases of vehicle manufacturer dominance than in those of battery manufacturer dominance. Finally, when a battery manufacturer is in the dominant position, a revenue sharing contract can incentivize supply chain coordination; when a vehicle manufacturer is in the dominant position, a two‐part tariff contract can realize coordination in the supply chain.

Two‐period supply chain coordination strategies with ambidextrous sustainable innovations

AbstractThis study considers a manufacturer with ambidextrous sustainable innovation capability selling products in environmentally conscious market through an independent retailer in a two‐period game setting. We design a two‐period game theoretic and dyadic supply chain (SC) model considering exploitative and exploratory nature of environmental innovations. We study five different contract types, namely, wholesale price contract, vertical Nash game structure, cost sharing contract, revenue sharing contract and two‐part tariff contract. We demonstrate the impact of market sensitivity towards sustainable innovation and cost parameters on optimal level of decision parameters. The equilibrium results reveal that a suitably designed two‐part tariff contract can be used to achieve coordination in a fragmented SC. The equilibrium results assist managers to optimise the SC based on the two‐period contract model. The results obtained in this study can help the decision‐makers to take decisions on investment in the ambidextrous sustainable innovation under different types of contract structures.

The 2020 Power Trading Agent Competition

This is the specification for the Power Trading Agent Competition for 2020 (Power TAC 2020). Power TAC is a competitive simulation that models a “liberalized” retail electrical energy market, where competing business entities or “brokers” offer energy services to customers through tariff contracts, and must then serve those customers by trading in a wholesale market. Brokers are challenged to maximize their profits by buying and selling energy in the wholesale and retail markets, subject to fixed costs and constraints; the winner of an individual “game” is the broker with the highest bank balance at the end of a simulation run. Costs include fees for publication and withdrawal of tariffs, for rectifying supply-demand imbalances, for contributions to peak demand, and for customer connections. The simulation environment models a wholesale market, a regulated distribution utility, and a population of energy customers, situated in a real location on Earth during a specific period for which weather data is available. The wholesale market is a relatively simple call market, similar to many existing wholesale electric power markets, such as Nord Pool in Scandinavia or FERC markets in North America, but unlike the FERC markets we are modeling a single region, and therefore we approximate the effects of locational-marginal pricing through manipulation of the wholesale supply curve. Customer models include households, electric vehicles, and a variety of commercial and industrial entities, many of whom have production capacity such as solar panels or wind turbines. All have “real-time” metering to support allocation of their hourly supply and demand to their subscribed brokers, and all are approximate utility maximizers with respect to tariff selection, although the factors making up their utility functions may include aversion to change and complexity that can retard uptake of marginally better tariff offers. A distribution utility models the regulated natural monopoly that owns the regional distribution network, and is responsible for maintenance of its infrastructure. Real-time balancing of supply and demand is managed by a market-based mechanism that uses economic incentives to encourage brokers to achieve balance within their portfolios of tariff subscribers and wholesale market positions, in the face of stochastic customer behaviors and weather-dependent renewable energy sources. Changes for 2020 are focused on stability and on making customer evaluation of regulation rates more realistic, and are highlighted by change bars in the margins. See Section 4.1.1 for details.

Non-intrusive load disaggregation model for residential consumers with Fourier series and optimization method applied to White tariff modality in Brazil

This paper proposes a novel approach to achieve better results for the Non-intrusive load disaggregation model for residential consumers. The proposed methodology considers two steps to face the problem. Firstly, periodical appliances are modelled by the Fourier series and extracted from the total power measured. Secondly, a Mixed Integer Linear Programming was proposed to disaggregate the remaining appliances. The paper presents results with a real case and indicates that the model can be useful for practical applications, such as the possibilty of the consumers of changing the modality of their tariff contract.

Do firms choose overcapacity or undercapacity in a vertical structure?

This study investigates capacity choice in a vertical structure in which each downstream firm makes its capacity decision, then a monopolistic upstream firm proposes the input price or two‐part tariff contract. Finally, each downstream firm chooses its output (or price). Contrary to the conventional wisdom that both firms hold excess capacity in an Cournot competition, we find that each downstream firm always chooses undercapacity regardless of both the nature of goods and the competition modes. Second, we also show that capacity efficiency is higher under Cournot competition than under Bertrand competition. Third, even though there are double marginalization distortion and rent‐extracting effect, we can achieve the monopoly equilibrium of the vertically integrated firm though two‐part tariff contract.

Resale Price Maintenance with Strategic Customers

We consider a decentralized supply chain (DSC) under resale price maintenance (RPM) selling a limited‐lifetime product to forward‐looking customers with heterogeneous valuations. When customers do not know the inventory level, double marginalization under RPM leads to a higher profit and aggregate welfare than without RPM under a two‐part tariff contract (TT). Both RPM and TT profits are higher and aggregate welfare is lower than in a centralized supply chain (CSC). When customers know the inventory, RPM coincides with CSC. Thus, overestimation of customer awareness may lead to overcentralization of supply chains with profit loss comparable with the loss from strategic customers. The case of RPM with unknown inventory is extended to an arbitrary number of retailers with inventory‐independent and inventory‐dependent demand. In both cases, the manufacturer, by setting a higher wholesale price, mitigates the inventory‐increasing effect of competition and reaches the same profit as with a single retailer. The high viability and efficiency of RPM in using double marginalization as a strategic‐behavior‐mitigating tool may serve as another explanation of why manufacturers may prefer DSC with RPM to a vertically integrated firm.

Testing Alternative Learning Theories: Evidence From Subscription Contracts

Analyzing panel data of 32,650 checking‐account holders facing a menu of three‐part tariff contracts, we document several findings that indicate that subscribers use simple heuristics to learn about the desirability of the contracts they have chosen. Our main findings are: subscribers change contracts in a direction that diminishes the probability of re–experiencing the trigger for switching; subscribers exhibit recency effects in switching; and after switching the majority of switchers systematically pay higher fees than they did before. We argue that directional learning theory best explains why consumers behave in a manner that yields suboptimal economic outcomes.

Simplicity is beauty: pricing coordination in two-product supply chains with simplest contracts under voluntary compliance

Pricing is an important topic in supply chains. In this paper, we explore the coordination challenge with the use of simplest contracts for the case with two products. To be specific, we consider a single-manufacturer single-retailer make-to-order supply chain. In the base model, we consider the case when the product demands are independent and derive the optimal pricing decisions under a decentralised setting. Comparing with the centralised setting, we show that double marginalisation in the decentralised setting reduces the supply chain efficiency by 25%. We then find that the simpler contracts such as the three-parameter two-part tariff (3P-TT) contract and the three-parameter revenue sharing (3P-RS) contract can coordinate the two-product supply chain. We extend the analysis in two cases: (i) the case when the products are substitutable and (ii) the case when the retailer is risk averse, and show that both the 3P-TT and 3P-RS contracts can coordinate the respective supply chain. We also find that the degrees of product substitution and retailer’s risk averse significantly influence the supply chain performance.

The 2017 Power Trading Agent Competition

This is the specification for the Power Trading Agent Competition for 2017 (Power TAC 2017). Power TAC is a competitive simulation that models a “liberalized” retail electrical energy market, where competing business entities or “brokers” offer energy services to customers through tariff contracts, and must then serve those customers by trading in a wholesale market. Brokers are challenged to maximize their profits by buying and selling energy in the wholesale and retail markets, subject to fixed costs and constraints; the winner of an individual “game” is the broker with the highest bank balance at the end of a simulation run. Costs include fees for publication and withdrawal of tariffs, and distribution fees for transporting energy to their contracted customers. Costs are also incurred whenever there is an imbalance between a broker’s total contracted energy supply and demand within a given time slot. The simulation environment models a wholesale market, a regulated distribution utility, and a population of energy customers, situated in a real location on Earth during a specific period for which weather data is available. The wholesale market is a relatively simple call market, similar to many existing wholesale electric power markets, such as Nord Pool in Scandinavia or FERC markets in North America, but unlike the FERC markets we are modeling a single region, and therefore we approximate locational-marginal pricing through a simple manipulation of the wholesale supply curve. Customer models include households, electric vehicles, and a variety of commercial and industrial entities, many of which have production capacity such as solar panels or wind turbines. All have “real-time” metering to support allocation of their hourly supply and demand to their subscribed brokers, and all are approximate utility maximizers with respect to tariff selection, although the factors making up their utility functions may include aversion to change and complexity that can retard uptake of marginally better tariff offers. The distribution utility models the regulated natural monopoly that owns the regional distribution network, and is responsible for maintenance of its infrastructure. Real-time balancing of supply and demand is managed by a market-based mechanism that uses economic incentives to encourage brokers to achieve balance within their portfolios of tariff subscribers and wholesale market positions, in the face of stochastic customer behaviors and weather-dependent renewable energy sources. Changes for 2017 are focused on a more realistic wholesale market, reducing the market power of brokers by making the simulation scenario into a relatively small part of a larger market, and are highlighted by change bars in the margins. See Section 5.3 for details.

The 2018 Power Trading Agent Competition

This is the specification for the Power Trading Agent Competition for 2018 (Power TAC 2018). Power TAC is a competitive simulation that models a liberalized retail electrical energy market, where competing business entities or offer energy services to customers through tariff contracts, and must then serve those customers by trading in a wholesale market. Brokers are challenged to maximize their profits by buying and selling energy in the wholesale and retail markets, subject to fixed costs and constraints; the winner of an individual game is the broker with the highest bank balance at the end of a simulation run. Costs include fees for publication and withdrawal of tariffs, and distribution fees for transporting energy to their contracted customers. Costs are also incurred whenever there is an imbalance between a broker’s total contracted energy supply and demand within a given time slot. The simulation environment models a wholesale market, a regulated distribution utility, and a population of energy customers, situated in a real location on Earth during a specific period for which weather data is available. The wholesale market is a relatively simple call market, similar to many existing wholesale electric power markets, such as Nord Pool in Scandinavia or FERC markets in North America, but unlike the FERC markets we are modeling a single region, and therefore we approximate locational-marginal pricing through a simple manipulation of the wholesale supply curve. Customer models include households, electric vehicles, and a variety of commercial and industrial entities, many of which have production capacity such as solar panels or wind turbines. All have “real-time” metering to support allocation of their hourly supply and demand to their subscribed brokers, and all are approximate utility maximizers with respect to tariff selection, although the factors making up their utility functions may include aversion to change and complexity that can retard uptake of marginally better tariff offers. The distribution utility models the regulated natural monopoly that owns the regional distribution network, and is responsible for maintenance of its infrastructure. Real-time balancing of supply and demand is managed by a market-based mechanism that uses economic incentives to encourage brokers to achieve balance within their portfolios of tariff subscribers and wholesale market positions, in the face of stochastic customer behaviors and weather-dependent renewable energy sources. Changes for 2018 are focused on stability, on simplifying interaction with the balancing market, and on encouraging more vigorous competition, and are highlighted by change bars in the margins. See Sections 3.1.2, 6, and 8.5 for details.

The 2016 Power Trading Agent Competition

This is the specification for the Power Trading Agent Competition for 2016 (Power TAC 2016). Power TAC is a competitive simulation that models a “liberalized” retail electrical energy market, where competing business entities or “brokers” offer energy services to customers through tariff contracts, and must then serve those customers by trading in a wholesale market. Brokers are challenged to maximize their profits by buying and selling energy in the wholesale and retail markets, subject to fixed costs and constraints; the winner of an individual “game” is the broker with the highest bank balance at the end of a simulation run. Costs include fees for publication and withdrawal of tariffs, and distribution fees for transporting energy to their contracted customers. Costs are also incurred whenever there is an imbalance between a broker’s total contracted energy supply and demand within a given time slot.The simulation environment models a wholesale market, a regulated distribution utility, and a population of energy customers, situated in a real location on Earth during a specific period for which weather data is available. The wholesale market is a relatively simple call market, similar to many existing wholesale electric power markets, such as Nord Pool in Scandinavia or FERC markets in North America, but unlike the FERC markets we are modeling a single region, and therefore we approximate locational-marginal pricing through a simple manipulation of the wholesale supply curve. Customer models include households, electric vehicles, and a variety of commercial and industrial entities, many of which have production capacity such as solar panels or wind turbines. All have “real-time” metering to support allocation of their hourly supply and demand to their subscribed brokers, and all are approximate utility maximizers with respect to tariff selection, although the factors making up their utility functions may include aversion to change and complexity that can retard uptake of marginally better tariff offers. The distribution utility models the regulated natural monopoly that owns the regional distribution network, and is responsible for maintenance of its infrastructure. Real-time balancing of supply and demand is managed by a market-based mechanism that uses economic incentives to encourage brokers to achieve balance within their portfolios of tariff subscribers and wholesale market posi- tions, in the face of stochastic customer behaviors and weather-dependent renewable energy sources.Changes for 2016 are focused on a more realistic cost model for brokers, and are highlighted by change bars in the margins. See Section 7 for details.

The 2015 Power Trading Agent Competition

This is the specification for the Power Trading Agent Competition for 2015 (Power TAC 2015). Power TAC is a competitive simulation that models a “liberalized” retail electrical energy market, where competing business entities or “brokers” offer energy services to customers through tariff contracts, and must then serve those customers by trading in a wholesale market. Brokers are challenged to maximize their profits by buying and selling energy in the wholesale and retail markets, subject to fixed costs and constraints. Costs include fees for publication and withdrawal of tariffs, and distribution fees for transporting energy to their contracted customers. Costs are also incurred whenever there is an imbalance between a broker’s total contracted energy supply and demand within a given time slot.The simulation environment models a wholesale market, a regulated distribution utility, and a population of energy customers, situated in a real location on Earth during a specific period for which weather data is available. The wholesale market is a relatively simple call market, similar to many existing wholesale electric power markets, such as Nord Pool in Scandinavia or FERC markets in North America, but unlike the FERC markets we are modeling a single region, and therefore we model locational-marginal pricing through a simple manipulation of the wholesale supply curve. Customer models include households, electric vehicles, and a variety of commercial and industrial entities, many of which have production capacity such as solar panels or wind turbines. All have “real-time” metering to support allocation of their hourly supply and demand to their subscribed brokers, and all are approximate utility maximizers with respect to tariff selection, although the factors making up their utility functions may include aversion to change and complexity that can retard uptake of marginally better tariff offers. The distribution utility models the regulated natural monopoly that owns the regional distribution network, and is responsible for maintenance of its infrastructure. Real-time balancing of supply and demand is managed by a market-based mechanism that uses economic incentives to encourage brokers to achieve balance within their portfolios of tariff subscribers and wholesale market positions, in the face of stochastic customer behaviors and weather-dependent renewable energy sources. The broker with the highest bank balance at the end of the simulation wins.

Contract efficiency for a decentralized supply chain in the presence of quality improvement

AbstractIn this paper, we study a joint pricing and product quality decision problem in a decentralized supply chain consisting of one manufacturer and one retailer. Although the manufacturer decides the product quality with an associated cost, the retailer decides the retail price. We aim to study and compare different contract formats for this decentralized supply chain. There is a trade‐off in the choice of contracts: simpler format contract (with a few parameters) is less complicated, but the contract efficiency is low. We start with the simplest one‐parameter contract: a wholesale price contract that serves as the benchmark. We then study how contract efficiency can be improved by adding one more parameter. Specifically, we consider three two‐parameter contracts that are commonly used in reality: two‐part tariff contract, revenue‐sharing contract, and effort cost sharing contract. We find that the contract efficiency is improved under all the three contracts, but in different ways: the improvement in contract efficiency under each of them dominates the other two when manufacturer's quality improvement effectiveness is relatively low, moderate, and high, respectively. Furthermore, through numerical examples, we find that under some cases, a choice from these three two‐parameter contracts can achieve a close‐to‐perfect efficiency (>85%). Finally, we investigate whether a combination of the three two‐parameter contracts can achieve coordination. Interestingly, we find that only the combination of effort cost sharing contract and revenue‐sharing contract can achieve coordination, whereas combinations of either of them and two‐part tariff contract cannot.

The 2014 Power Trading Agent Competition

This is the specification for the Power Trading Agent Competition for 2014 (Power TAC 2014). Power TAC is a competitive simulation that models a “liberalized” retail electrical energy market, where competing business entities or “brokers” offer energy services to customers through tariff contracts, and must then serve those customers by trading in a wholesale market. Brokers are challenged to maximize their profits by buying and selling energy in the wholesale and retail markets, subject to fixed costs and constraints. Costs include fees for publication and withdrawal of tariffs, and distribution fees for transporting energy to their contracted customers. Costs are also incurred whenever there is an imbalance between a broker’s total contracted energy supply and demand within a given time slot. The simulation environment models a wholesale market, a regulated distribution utility, and a population of energy customers, situated in a real location on Earth during a specific period for which weather data is available. The wholesale market is a relatively simple call market, similar to many existing wholesale electric power markets, such as Nord Pool in Scandinavia or FERC markets in North America, but unlike the FERC markets we are modeling a single region, and therefore we model locational-marginal pricing through a simple manipulation of the wholesale supply curve. Customer models include households, electric vehicles, and a variety of commercial and industrial entities, many of which have production capacity such as solar panels or wind turbines. All have “real-time” metering to support allocation of their hourly supply and demand to their subscribed brokers, and all are approximate utility maximizers with respect to tariff selection, although the factors making up their utility functions may include aversion to change and complexity that can retard uptake of marginally better tariff offers. The distribution utility models the regulated natural monopoly that owns the regional distribution network, and is responsible for maintenance of its infrastructure. Real-time balancing of supply and demand is managed by a market-based mechanism that uses economic incentives to encourage brokers to achieve balance within their portfolios of tariff subscribers and wholesale market positions, in the face of stochastic customer behaviors and weather-dependent renewable energy sources. The broker with the highest bank balance at the end of the simulation wins.