Honesty in an Online Labor Market.

The efficient functioning of markets and institutions assumes a certain degree of honesty from participants. In labor markets, for instance, employers benefit from employees who will render meaningful work, and employees benefit from employers who will pay the promised amount for services rendered. We use an established method for detecting dishonest behavior in a series of experiments conducted on Amazon's Mechanical Turk, a popular online labor market. Our first experiment estimates a baseline amount of dishonesty for this task in this population. The second experiment tests the hypothesis that the level of dishonesty in the population will be sensitive to the relative amount that can be gained by dishonest reporting, and the third experiment, manipulates the degree to which dishonest reporting can be detected at the individual level. We conclude with a demographic and crosscultural analysis of the predictors of dishonest reporting in this market.