Browsing by Author "Zhong, Huizhen"

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    Do participation rates vary with participation payments in laboratory experiments?
    (University of Alabama Libraries, 2024) Zhong, Huizhen; Deck, Cary; Henderson, Daniel
    This paper reports a series of experiments designed to evaluate how the advertised participation payment impacts participation rates in laboratory experiments. Our initial goal was to generate variation in the participation rate as a means to control for selection bias when evaluating treatment effects in common laboratory experiments. Initially, we varied the advertised participation payment to 1734 people from $5 to $15 using standard email recruitment procedures, but found no statistical evidence this impacted the participation rate. A second study increased the advertised payment up to $100. Here, we find marginally significant statistical evidence that the advertised participation payment affects the participation rate when payments are large. To combat skepticism of our results, we also conducted a third study in which verbal offers were made. Here, we found no statistically significant increase in participation rates when the participation payment increased from $5 to $10. Finally, we conducted an experiment similar to the first one at a separate university. We found no statistically significant increase in participation rates when the participation payment increased from $7 to $15. The combined results from our four experiments suggest moderate variation in the advertised participation payment from standard levels has little impact on participation rates in typical laboratory experiments. Rather, generating useful variation in participation rates likely requires much larger participation payments and/or larger potential subject pools than are common in laboratory experiments.
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    Nonparametric Estimation and Inference in the Presence of Sample Selection Bias in Experimental Economics Studies
    (University of Alabama Libraries, 2021) Zhong, Huizhen; Henderson, Daniel J.; University of Alabama Tuscaloosa
    Experimental economics studies usually involve self-selection behaviors. In this dissertation, we explore the use of nonparametric approaches to estimate the treatment effect in these studies in the presence of sample selection bias. The first chapter reviews the econometrics literature on nonparametric estimation of treatment effects under sample selection. Specifically, we focus on the Heckman (1979) two-step correction approach, its nonparametric extensions, and three bounding estimation approaches: Horowitz and Manski (2000), Lee (2009), and Behaghel et al. (2015). We also discuss the different estimands and the relative performance in these studies. The second chapter explores the treatment effect of a higher match ratio on an individual’s donation behavior based on evidence from a field experiment using multiple waves of email solicitations. Since donation decisions are observable only for email openers and opening rates differ between treatment and control groups, we apply the nonparametric bounding estimation approaches of Lee (2009) and Behaghel et al. (2015) to correct for selection bias when estimating the treatment effect. A higher match rate significantly increases an email opener’s likelihood to give and increases the donation amount for those who contributed to the fund in the past 24 months. The third chapter investigates whether randomized advertised show-up fees can be used as an exclusion restriction in the Heckman (1979) correction model to correct for bias caused by individuals’ self-selection into lab experiment studies. We control for the actual participation fee and study the impact of the advertised show-up fee on an individual’s participation decision, subject’s decision making, and the treatment effects in three well-studied lab experiment tasks. We estimate these impacts using nonparametric regressions. For the range of show-up fees in our study, we find no impact on an individual’s participation decision. Also, the advertised show-up fee does not affect the participant’s decision-making or the treatment effect in the tasks related to individuals' social preference and risk attitude. However, the advertised show-up fee impacts subjects’ strategic performance under a higher cognitive load. Therefore, caution should be made when we incorporate the randomized advertised show-up fee in the experiment design to correct for participation bias.