Collaborative Research: Meta-Analysis of Empirical Estimates of Loss-Aversion

损失比收益带来的伤害更大，感觉更好。这一原理被称为“损失厌恶”，是行为经济学的一项基本原理，它利用心理学来改进经济预测和建议。损失厌恶被用来理解几个政策问题，包括国际贸易和股票市场交易。它还被政府和企业用作设计“轻推”的工具，帮助人们做出更好的决定。“损失厌恶”的价值取决于它所适用的具体事件和情况。因此，存在几个计算出的损失厌恶值，为其在研究和政策中的使用造成了混乱和不确定性。这项拟议的研究将收集所有计算的损失厌恶价值，分类和总结，并将它们放在一个中央储存库，以供参考。这一研究活动的结果将提高损失厌恶在研究和政策制定中的有用性。这将确立美国在损失厌恶研究和应用方面的全球领先地位，因为这将是世界上唯一的此类研究。对亏损的厌恶是用一个数字“lambda”来衡量的，它被认为是收益价值的1.5-2倍。这一范围的数值是基于突出的例子，但实际上有数百项已发表的研究衡量了“lambda”。我们提出的荟萃分析涉及找到和识别每一篇统计估计为“lambda”的论文，记录所有这些估计，然后总结他们的结果。然后，科学家们可以第一次看到假设的“lambda”=1.5到2的范围是否正确。这项拟议的研究还将根据科学方法的不同(例如，受控实验室实验与日常决策的推论)来衡量不同人群的“lambda”差异程度。这些证据可以帮助行为洞察团队为特定的人群定制轻推，使用关于“lambda”的适当价值的更精确的假设。元分析还说明了如何有效地积累科学知识，以克服可能存在的对科学重现性的担忧。这项研究还有助于提高科学研究的重现性。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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 "lambda", 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 "lambda". Our proposed meta-analysis involves finding and identifying every paper that has a statistical estimate of "lambda", recording all those estimates, then summarizing their results. Scientists can then see for the first time whether the assumed range of "lambda"=1.5 to 2 is correct. The proposed research will also measure how much "lambda" 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 "lambda". 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.