Yi LiFollow

Date of Award

Summer 8-1-2016

Degree Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Economics

First Advisor

James. C. Cox

Second Advisor

Yongsheng Xu

Third Advisor

Vjollca Sadiraj

Fourth Advisor

Ajay Subramanian

Abstract

This dissertation focuses on one methodological problem in experimental economics: how to pay subjects, or in other words, payoff mechanism issue. For example, one popular payoff mechanism is Random Lottery Incentive System (RLIS), that is, after subjects have made all their choices, one among all their decisions is randomly selected as their payment. Payoff mechanism is part of the incentive in economic experiments, and people respond to incentives. When economists think they observe subjects' responses to experimental tasks, the observed behavior is actually the joint effect of the decision tasks and the payoff mechanism used in the experiment. When the payoff mechanism is not incentive compatible, it may distort behavior. Cox et al.(2015) have shown that with the same decision tasks, when payoff mechanism changes, people make different choices.

When it comes to the experiments of decision making under risk, Holt(1986) firstly points out that RLIS is incentive compatible when Independence Axiom is assumed. Independence Axiom says that if one prefers Gamble A to Gamble B, when both gambles mix the same proportion with a third Gamble C, one would prefer the combination of A and C to B and C. Independence is one of the fundamental axioms of Expected Utility Theory (EUT). Later decision models relax such axiom and allow violations of Independence, such as Rank Dependent Utility Theory (RDU) and Cumulative Prospect Theory (CPT). Therefore, when RDU or CPT is assumed, RLIS becomes incentive incompatible. It is well-known in the literature that one round task, where subjects make one decision and get paid by their choice, is incentive compatible with all the decision models. Harrison and Swarthout (2014) have shown that when RDU is assumed, the estimation from the data using RLIS is different from that using one round task. The gap in the literature is that under a multiple-round setting, it's still mysterious what incentive compatible payoff mechanisms are for the general risk theories.

In the dissertation, I discuss the incentive compatible payoff mechanisms for general risk models. The ``general'' refers to the models where well-behaved (complete and transitive) preference is assumed. In Chapter 1, I propose a new payoff mechanism, which I call ``Accumulative Best Choice'' (ABC) design and show that it is incentive compatible with general risk models. I also test the validity of ABC in the lab. The data from two experiments show that there is no significant difference for the choices or the estimates under ABC design and under one round task at 5\% level. In Chapter 2, with the data from ABC design, I estimate the structural parameters for both EUT and RDU models, which is so far the first using an incentive compatible payoff mechanism for RDU model under a multiple-round setting. In Chapter 3, I apply ABC design on the most popular risk attitude elicitation method: Holt and Laury (HL) (2002) multiple price list. With estimations for EUT, Dual Theory of Expected Utility (DU) and RDU, I show that the HL list offers more information about the probability curvature than the utility curvature.

DOI

https://doi.org/10.57709/8903763

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