Measuring and bounding experimenter demand

We propose a technique for assessing robustness of behavioral measures and treatment effects to experimenter demand effects. The premise is that by deliberately inducing demand in a structured way we can measure its influence and construct plausible bounds on demand-free behavior. We provide formal restrictions on choice that validate our method, and a Bayesian model that microfounds them. Seven pre-registered experiments with eleven canonical laboratory games and around 19,000 participants demonstrate the technique. We also illustrate how demand sensitivity varies by task, participant pool, gender, real versus hypothetical incentives, and participant attentiveness, and provide both reduced-form and structural analyses of demand effects.