Estimating Effects of Incentive Contracts in Online Labor Platforms
提出一种从激励合同和结果数据中估计委托代理模型的方法，重点估计代理人的生产函数，并通过众包平台实验验证其有效性，对平台设计绩效薪酬有参考价值。
The design of performance-based incentives—commonly used in online labor platforms—can be naturally posed as a moral hazard principal-agent problem. In this setting, a key input to the principal’s optimal contracting problem is the agent’s production function: the dependence of agent output on effort. Although agent production is classically assumed to be known to the principal, this is unlikely to be the case in practice. Motivated by the design of performance-based incentives, we present a method for estimating a principal-agent model from data on incentive contracts and associated outcomes, with a focus on estimating agent production. The proposed estimator is statistically consistent and can be expressed as a mathematical program. To circumvent computational challenges with solving the estimation problem exactly, we approximate it as an integer program, which we solve through a column generation algorithm that uses hypothesis tests to select variables. We show that our approximation scheme and solution technique both preserve the estimator’s consistency and combine to dramatically reduce the computational time required to obtain sound estimates. To demonstrate our method, we conducted an experiment on a crowdwork platform (Amazon Mechanical Turk) by randomly assigning incentive contracts with varying pay rates among a pool of workers completing the same task. We present numerical results illustrating how our estimator combined with experimentation can shed light on the efficacy of performance-based incentives. This paper was accepted by Chung Piaw Teo, optimization. Supplemental Material: The data files and e-companion are available at https://doi.org/10.1287/mnsc.2022.4450 .