Three Essays on Dynamic Incentive Problems in Operations Management


Student thesis: Doctoral Thesis

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Award date15 Feb 2023


This thesis consists of three parts. First, we study a procurement problem where the supplier holds superior cost information and can learn to improve efficiency over time. Despite its prevalence, the supply chain literature provides limited guidance on how to manage learning suppliers with evolving private information. We show that supplier learning has both efficiency and agency effects, it can induce countervailing incentives, and the agency effect can overwhelm the efficiency effect. As a result, (i) supplier learning can hurt profits, (ii) information asymmetry can improve efficiency, (iii) production distortion can go upward, and (iv) ignoring the agency effect of learning can mislead contract design and inflict severe losses. As such, previous studies may have overlooked the dark side of learning and overestimated the harm of information asymmetry. Our results shed light on when and why firms suppress learning, defer payments, price below cost, overproduce output, and voluntarily disclose private information. We help explain why suppliers in labor-intensive industries often suffer harsh treatment in the early stages of the relationship. By highlighting the strategic role of supplier learning, this study sharpens our understanding of procurement theory and practice.

Second, we study the optimal payment for dynamic treatment regimes. Dynamic treatment regimes improve health outcomes by tailoring each treatment to evolving patient conditions, but they also allow providers to learn and game the system over time. How should insurers pay? We study this new class of reimbursement problems, where the provider can privately learn and manipulate the progression of the patient's condition. (i) We characterize the optimal payment policy: it rewards provider honesty with incentive pay and elicits future private information with recursive deferred payment; it internalizes two intertemporal externalities of each treatment; moreover, it admits a simple implementation of risk-adjusted cost-sharing policy. (ii) We show that by ignoring dynamic learning and gaming, the existing payment models may have overestimated the harm of information asymmetry. Using the optimal policy, insurers only need to pay for initial private information; they can exploit provider uncertainty and elicit future private information at no cost. (iii) Our study informs U.S. healthcare payment reform: the analytical results provide {actionable insights}; our study also quantifies when and why the optimal policy outperforms the existing ones using two sets of real data. By highlighting the critical role of dynamic learning and gaming, this study advances our understanding of healthcare payment theory and practice.

Third, we study the optimal contract for Medicare to regulate long-term care hospitals' (LTCHs') strategic discharging. LTCHs serve post-acute patients for extended stays. The existing payment policy entails a sharp jump at the threshold of the short-stay outlier (SSO). Specifically, LTCHs strategically retain the patients to pass the SSO and collect more reimbursement, causing a "magic day" effect. How should Medicare revise the contract to regulate LTCHs' strategic discharging behavior and optimize patient outcomes? This study focuses on designing such a new class of contract. We build a model where LTCHs can privately observe the patient's severity and control discharge decision responses to the Medicare contract. (i) We characterize the optimal first- and second-best contracts corresponding to the full and asymmetric information regimes. The optimal second-best discharging policy belongs to a severity history-dependent cutoff type, and the optimal payment policy has a deferred payment structure. (ii) We offer managerial insights. The optimal second-best contract delays discharging patients, and such distortion persists regardless of length-of-stay (LOS). Moreover, we construct a simple contract that depends on the patient's initial severity and LOS, which implements the second-best discharging policy when severity follows the AR(1) process. (iii) We quantify the performance gaps between the optimal contract and three alternatives using real data. By highlighting the role of the LTCHs' gaming behavior under dynamic information asymmetry, this study advances the understanding of healthcare payment reform regulating the LTCHs' strategic discharge.