A reassessment of the potential for loss-framed incentive contracts to increase productivity: a meta-analysis and a real-effort experiment

A-Tier
Journal: Experimental Economics
Year: 2022
Volume: 25
Issue: 5
Pages: 1441-1466

Score contribution per author:

2.011 = (α=2.01 / 2 authors) × 2.0x A-tier

α: calibrated so average coauthorship-adjusted count equals average raw count

Abstract

Abstract Substantial productivity increases have been reported when incentives are framed as losses rather than gains. Loss-framed contracts have also been reported to be preferred by workers. The results from our meta-analysis and real-effort experiment challenge these claims. The meta-analysis’ summary effect size of loss framing is a 0.16 SD increase in productivity. Whereas the summary effect size in laboratory experiments is a 0.33 SD, the summary effect size from field experiments is 0.02 SD. We detect evidence of publication biases among laboratory experiments. In a new laboratory experiment that addresses prior design weaknesses, we estimate an effect size of 0.12 SD. This result, in combination with the meta-analysis, suggests that the difference between the effect size estimates in laboratory and field experiments does not stem from the limited external validity of laboratory experiments, but may instead stem from a mix of underpowered laboratory designs and publication biases. Moreover, in our experiment, most workers preferred the gain-framed contract and the increase in average productivity is only detectable in the subgroup of workers (~ 20%) who preferred the loss-framed contracts. Based on the results from our experiment and meta-analysis, we believe that behavioral scientists should better assess preferences for loss-framed contracts and the magnitude of their effects on productivity before advocating for greater use of such contracts among private and public sector actors.

Technical Details

RePEc Handle
repec:kap:expeco:v:25:y:2022:i:5:d:10.1007_s10683-022-09754-x
Journal Field
Experimental
Author Count
2
Added to Database
2026-01-25