Ambiguity in Performance Pay: An Online Experiment

Abstract: Many incentive plans do not include an explicit mapping between workers’ performance and compensation. Potential employees therefore face both risk and uncertainty when choosing whether to accept an employment contract. Using an online labor market, Amazon Mechanical Turk, we study the effect of ambiguity on the willingness of individuals to accept a contract to perform a real-effort task as well as the effect on their performance at this task. We manipulate the information potential workers have about how a performance bonus is assigned and their ability at the task. Ambiguity about the relationship between a worker’s performance and their bonus affects both the willingness of individuals to accept a contract and their likelihood of quitting the task prior to completion, but has little effect on performance. Oddly, low levels of ambiguity decrease the likelihood of accepting the contract but moving to higher levels of ambiguity increase the likelihood of acceptance. Information about ability at the task reduces willingness to accept a contract and increases the likelihood of quitting, but does not affect task performance.