Counterfactual Assessment Methods and Outcome-Based Contracts: A Formal Model Approach

Despite the increasing use of outcome-based contracts to incentivize effective private and nonprofit delivery of public services, imperfect assessment may trigger payments for outcomes that were not caused by the service providers. A possibility to mitigate this problem is linking these payments with measured outcomes that adjust for counterfactual assessment of what would have happened without the intervention, as in the case of randomized controlled trials and other impact evaluation methods comparing the outcomes of treated and control groups. However, few outcome-based contracts adopt payment rules based on counterfactual assessment techniques. A potential explanation emerging from the extant literature is that these techniques are costly, time-consuming, and complex. In this paper, we move beyond this explanation through a formal model that integrates the literatures of incentives and policy evaluation. We show that counterfactual assessment contracts may undermine effort when the number of treated subjects is small and there is limited investment per treated subject. Our formal model predicts that increased experience of the contract sponsors may reduce the adoption of counterfactual assessment. Evidence from computer simulation and from a worldwide database of outcome-based contracts are aligned with our predictions and suggest that counterfactual assessment contracts are not always optimal to reward private operators of public services. By identifying the boundary conditions under which counterfactual assessment methods are used in incentive contracts in the public sector, this work informs the literatures of outcome-based contracts and performance-based systems and highlights the importance of using formal models in developing theory on novel public administration phenomena