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Jared Rubin, Anya Samek, Roman Sheremeta

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Firms face an optimization problem that requires a maximal quantity output given a quality constraint. How firms should incentivize quantity and quality to meet these dual goals remains an open question. We provide a theoretical model and conduct an experiment in which participants are paid for both quantity and quality of a real effort task. Consistent with the theoretical predictions, higher quality incentives encourage participants to shift their attention from quantity to quality and to decrease the error rate at the expense of lowering quantity of output. This quantity-quality trade-off is significantly impacted by the participant's ability and level of loss aversion.