Doctoral Dissertation Research in Economics: Reference-Dependent Effort Provision under Heterogeneous Gain Loss Attitudes

理解个人如何在不确定性面前做出决定是经济学研究中的一个核心问题。虽然早期的经济理论假设完全理性，但研究人员通过引入参考依赖决策的思想，在这些经典模型的基础上取得了进展。这些模型植根于这样一种直觉，即个人将自己的结果与某个参照点进行比较，并已在许多背景下使用，从职业高尔夫球手的推杆行为(Pope和Schweitzer，2011)到失业工人的求职行为(DellaVigna等人，2017)。然而，最初的依赖于参考的模型有一个主要缺点：由于没有指定固定的参考点，它们留下了相当大的自由度。理论的进步试图解决这个问题，最终形成了基于期望的参照依赖模型(值得注意的是，Koszegi和Rabin，2006-KR)。通过将参照点内化为理性预期，该模型产生了新的、可检验的含义，获得了混合实验的支持。然而，这些先前的研究可能会被普遍的损失厌恶假设所混淆，在这个假设中，低于参考点的结果被认为比高于参考点的结果更糟糕。与这一假设相反的是，经验和实验证据表明，大约30%的人相反，他们是“贪得无厌”的。重要的是，作者先前的工作表明，一小部分喜欢收获的参与者可以歪曲对该模型的总体预测(Goette等人，2019年)。在这个项目中，研究团队将提供额外的证据，证明这些得失态度的异质性如何混淆KR测试，并设计一个实验范式，在努力提供的背景下克服这些混淆。在考虑一些关键的政策问题时，更好地理解得失偏好的分布是特别重要的，因为这些依赖于参考的模型在失业保险和健康保险决策等环境中被证明是非常有价值的。为了实现所述目标，本项目将专注于更新现有的实验范式，以解决得失态度的混乱，即得失态度的异质性。因此，提出了一个两阶段实验，其中从第一阶段的选择中恢复参与者的得失态度的测量，并在第二阶段检验假设。具体地说，参与者首先会被问到这样的问题：你愿意以X的工资工作多少任务？工资要么是确定性的(例如，每项任务20美分)，要么是随机的(例如，50%的机会，工资将是每项任务10美分，50%的可能性是每项任务30美分)。这种变化将允许作者建立一个结构模型，并恢复与努力成本函数一起的得失态度的关键行为参数。在第二阶段，参与者被问到了一个略有不同的问题，改编自Abeler等人(2011)：如果你的工资是每项任务25美分的50%的可能性，以及无论任务数量的50%的Y美元的可能性，你愿意工作多少？关键的待遇将是随机变化的Y，从小(5美元)到大(20美元)。KR预测，随着Y的增加，厌恶损失的代理人应该工作得更努力，而喜欢收益的代理人应该工作得更少。通过使用之前测量的得失态度，该项目能够直接测试不同偏好的个体是否像理论预测的那样做出不同的反应。由此产生的证据将有助于澄清先前范式的混合结果，并提供证据表明，在控制了得失态度中令人困惑的异质性后，KR是否可以预测行为。这一裁决反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

Understanding how individuals make decisions in the face of uncertainty is a core issue in economic research. While earlier economic theory postulated full rationality, researchers have advanced upon these classical models by introducing the idea of reference-dependent decision making. These models, rooted in the intuition that individuals compare their outcomes to some reference point, have been used in a number of contexts, ranging from the putting behavior of professional golfers (Pope and Schweitzer, 2011) to the job search behavior of unemployed workers (DellaVigna et al, 2017). The original reference-dependent models suffered from one major drawback, however: in failing to specify a fixed reference point, they left open a substantial degree of freedom. Theoretical advancements sought to shut this downfix this problem, culminating in models of expectations-based reference dependence (notably, Koszegi and Rabin, 2006 – KR). By endogenizing the reference point as rational expectations, this model generated new and, testable implications, garnering mixed experimental support. However, these prior studies are potentially confounded by an assumption of universal loss aversion, in which outcomes falling below the reference point are assumed to feel worse than those above the reference point. Contrary to this assumption, empirical and experimental evidence suggests that roughly 30% of individuals are instead “gain -loving”.” Importantly, prior work by the authors suggests that a small minority of gain -loving participants can skew aggregate predictions of the model (Goette et al, 2019). In this project, the research team will provide additional evidence of how heterogeneity in these gain-loss attitudes confounds tests of KR, and design an experimental paradigm to overcome these confounds in the context of effort provision. A better understanding of the distribution of gain-loss preferences is particularly important when considering a number of key policy questions, as these reference-dependent models have proven to be invaluable in settings such as unemployment insurance and health insurance decisions.In order to accomplish the stated goals, this project will focus on updating an existing experimental paradigm so as to account for the confound ofthat is heterogeneity in gain-loss attitudes. A two-stage experiment is thus proposed, wherein a measure of the participant’s gain-loss attitude is recovered from first stage choices, and the hypothesis is tested in the second. Specifically, participants will first be asked questions of the form: “How many tasks are you willing to work at wage X?”. The wages will either be deterministic (e.g. 20 cents per task) or stochastic (e.g. with 50% chance, the wage will be 10 cents per task and with 50% chance it will be 30 cents per task). This variation will allow the authors to build a structural model and recover the key behavioral parameter of gain-loss attitudes alongside a cost of effort function. Participants are asked a slightly different question in the second stage, adapted from Abeler et al (2011): “How much are you willing to work if your wage is a 50% chance of 25 cents per task, and a 50% chance of Y dollars regardless of the number of tasks?”. The key treatment will be randomly varying Y from a small ($5) to a large amount ($20). KR predicts that loss averse agents should work harder as Y increases, while gain loving agents should work less hard. By using the previously measured gain-loss attitude, this project is able to directly test whether individuals with different preferences respond differently as predicted by the theory. The resulting evidence will help clarify the mixed results from the prior paradigm, as well as providing evidence of whether KR is predictive of behavior after controlling for the confounding heterogeneity in gain-loss attitudes.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.