Score contribution per author:
α: calibrated so average coauthorship-adjusted count equals average raw count
We investigate time discounting under risk. To this end, we modify a popular multiple price list (MPL) design to elicit time discounting. Structural estimations of model parameters yield several new insights. For one, we find present bias to persist under risk, contrary to some previous evidence from the psychology literature. We further confirm the robustness of inverse-S shaped probability weighting. This is important inasmuch as random choice predicts the opposite shape in our setup. We also show that correcting for probability weighting under risk impacts the assessment of discount rates. Those are systematically underestimated under the commonly used, more restrictive, expected utility.