A/B Contracts

This paper aims to improve the practical applicability of the classic theory of incentive contracts under moral hazard. We establish conditions under which the information provided by an A/B test of incentive contracts is sufficient for answering the question of how best to improve a status quo incentive contract, given a priori knowledge of the agent’s monetary preferences. We assess the empirical relevance of this result using data from DellaVigna and Pope’s (2018) study of a variety of incentive contracts. Finally, we discuss how our framework can be extended to incorporate additional considerations beyond those in the classic theory. (JEL D82, D86, D91)

Compensation incentive contract of the subject librarians based on the H-M model

Based on the H-M model, the compensation incentive model is constructed under the inter-task cost function. The best compensation incentive contract is constructed by solving the incentive model, and the incentive characteristics are analysed. The results show that the best incentive intensity decreases as the subject service selectivity increases. The higher incentive intensity of university managers for specific tasks, the lower efforts of subject librarians for another specific task. Moreover, when the tasks are substituted for each other, the profit-sharing ratios corresponding to different tasks are complementary. Finally, we establish the econometric empirical models to test these results.

Generation Revenue Regulation and Incentive Contract Design in Electricity Market

In order to curb market power, encourage investment and redistribute welfare, revenue regulation should be carried out in electricity market. The incentive contract represented by the Contract for Difference is a kind of regulation. This paper proposes a Government Authorized Contract based on Revenue Estimation Method (REM) from the perspective of regulating generation revenue. First, the principle and design of the Vesting Contract in Singapore electricity market is employed, analyzing its limitations in regulation. Then, the incentive contract based on REM is presented in four steps. Finally, the IEEE30 node classic system is adopted to demonstrate the proposed model, showing that it can promote generators to bid rationally.

Collaborative Innovation with Dynamic Incentive Contracts in a Supply Chain

The collection and sharing of consumers’ knowledge by retailers can help manufacturers improve the innovation level of products, thereby improving the performance of supply chain. However, due to the cost of collecting consumers’ knowledge, the wholesale price contract can no longer coordinate supply chain members effectively. It is necessary to study the problem how the retailers are encouraged to make more efforts for the cooperative innovation with manufacturers. This paper introduces two dynamic incentive contracts for improving collaborative innovation level in a two-player supply chain, and the impacts of these contracts on supply chain’s performance are investigated, by using a Stackelberg differential game model. The manufacturer, as a Stackelberg leader, determines the R&D investment while the retailer is responsible for the retail price and the efforts in collection of the consumer’s information (or preference) to the products. The model incorporates a wholesale price contract and two incentive contracts to better understand how the manufacturer can facilitate the retailer’s efforts in the collection of consumer’s information and increase the profits of the members of supply chain. Our results suggest that the optimal profit of the supply chain, the retailer’s efforts in the collection of consumer’s knowledge, the retail price, and the innovation level under the reward incentive contract are higher than their counterparts in other contracts. In particular, the retailer’s optimal effort under the reward incentive contract is even higher than the one in the centralized decision scenario. However, if the manufacturer commits an effort target to the retailer, it shows that the retailer’s optimal effort is independent of the target. The manufacturer’s optimal R&D investments are constants in the three contracts under the dynamic setting. Furthermore, numerical simulations show that the effort target has little impact on profits of the supply chain although it affects the decision making of supply chain members to some extent, whereas the retailer’s marginal reward offered by the manufacturer influences the innovation level of product and the supply chain’s profit significantly.