Crowdsourcing leverages the diverse skill sets of large collections of individual contributors to solve problems and execute projects, where contributors may vary significantly in experience, expertise, and interest in completing tasks. Hence, to ensure the satisfaction of its task requesters, most existing crowdsourcing platforms focus primarily on supervising contributors' behavior. This lopsided approach to supervision negatively impacts contributor engagement and platform sustainability. In this paper, we introduce rating mechanisms to evaluate requesters' behavior, such that the health and sustainability of crowdsourcing platform can be improved. We build a game theoretical model to systematically account for the different goals of requesters, contributors, and platform, and their interactions. On the basis of this model, we focus on a specific application, in which we aim to design a rating policy that incentivizes requesters to engage less-experienced contributors. Considering the hardness of the problem, we develop a time efficient heuristic algorithm with theoretical bound analysis. Finally, we conduct a user study in Amazon Mechanical Turk (MTurk) to validate the central hypothesis of the model. We provide a simulation based on 3 million task records extracted from MTurk demonstrating that our rating policy can appreciably motivate requesters to hire less-experienced contributors.