Collaborative Research: Meta-Analysis of Empirical Estimates of Loss-Aversion
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Losses hurt more than gains feel good. This principle, known as ??loss-aversion??, is a foundational principle in behavioral economics, which uses psychology to improve economic prediction and advice. Loss aversion is applied to understand several policy issues, including international trade and stock market trades. It has also been used as a tool by governments and businesses to design ??nudges?? to help people make better decisions. The value of ??loss aversion?? depends on specific events and circumstances in which it is applied. As a result, several calculated values of loss aversion exist, creating confusion and uncertainty for its use in research and policy. This proposed research will collect all calculated values of loss aversion, categorize and summarize, and placed them in a central repository for easy reference. The results of this research activity will improve the usefulness of loss aversion in research and policy development. This will establish the US as the global leader in loss aversion research and application as this will be the only such study in the world. Aversion to losses is measured by a number ??, which is thought to be about 1.5-2 times higher than the value of gains. This range of values is based on prominent examples, but there are actually several hundred published studies measuring ??. Our proposed meta-analysis involves finding and identifying every paper that has a statistical estimate of ??, recording all those estimates, then summarizing their results. Scientists can then see for the first time whether the assumed range of ??=1.5 to 2 is correct. The proposed research will also measure how much ?? varies for different groups of people and based on differences in scientific methods (e.g., controlled lab experiments vs. inferences from everyday decisions). This evidence can help behavioral insights teams customize nudges for particular groups of people, using more precise assumptions about the appropriate value of ??. The meta-analysis also illustrates how to cumulate scientific knowledge effectively, to overcome possible concerns about the reproducibility of science. This research also contributes to the efforts to increase reproducibility of scientific research.This award reflects NSF''s statutory mission and has been deemed worthy of support through evaluation using the Foundation''s intellectual merit and broader impacts review criteria.