Give Me a Challenge or Give Me a Raise

Abstract

I study the effect of task difficulty on workers' effort and compare it to the effect of monetary rewards in an incentivized lab experiment. I find that task difficulty has an inverse-U effect on effort, and that this effect is quantitatively large when compared to the effect of conditional monetary rewards. Difficulty acts as a mediator of monetary rewards: conditional rewards are most effective at the intermediate or high levels of difficulty. I show that the inverse-U pattern of effort response to difficulty is not consistent with the Expected Utility model but is consistent with the Rank-Dependent Utility model that allows for non-linear probability weighting. I structurally estimate the model and find that it successfully captures the treatment effects observed in the data. I discuss the implications of my findings for the design of optimal incentive schemes for workers and modeling effort.

The Economics of Babysitting a Robot

“…The additively separable form, while being analytically convenient, is also somewhat restrictive because it does not allow for complementarity or substitutability of effort and money in the utility function(Alekseev, 2020).…”