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"experimental economics"
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Multiple hypothesis testing in experimental economics
The analysis of data from experiments in economics routinely involves testing multiple null hypotheses simultaneously. These different null hypotheses arise naturally in this setting for at least three different reasons: when there are multiple outcomes of interest and it is desired to determine on which of these outcomes a treatment has an effect; when the effect of a treatment may be heterogeneous in that it varies across subgroups defined by observed characteristics and it is desired to determine for which of these subgroups a treatment has an effect; and finally when there are multiple treatments of interest and it is desired to determine which treatments have an effect relative to either the control or relative to each of the other treatments. In this paper, we provide a bootstrap-based procedure for testing these null hypotheses simultaneously using experimental data in which simple random sampling is used to assign treatment status to units. Using the general results in Romano and Wolf (Ann Stat 38:598–633,
2010
), we show under weak assumptions that our procedure (1) asymptotically controls the familywise error rate—the probability of one or more false rejections—and (2) is asymptotically balanced in that the marginal probability of rejecting any true null hypothesis is approximately equal in large samples. Importantly, by incorporating information about dependence ignored in classical multiple testing procedures, such as the Bonferroni and Holm corrections, our procedure has much greater ability to detect truly false null hypotheses. In the presence of multiple treatments, we additionally show how to exploit logical restrictions across null hypotheses to further improve power. We illustrate our methodology by revisiting the study by Karlan and List (Am Econ Rev 97(5):1774–1793,
2007
) of why people give to charitable causes.
Journal Article
Permutation tests for experimental data
This article surveys the use of nonparametric permutation tests for analyzing experimental data. The permutation approach, which involves randomizing or permuting features of the observed data, is a flexible way to draw statistical inferences in common experimental settings. It is particularly valuable when few independent observations are available, a frequent occurrence in controlled experiments in economics and other social sciences. The permutation method constitutes a comprehensive approach to statistical inference. In two-treatment testing, permutation concepts underlie popular rank-based tests, like the Wilcoxon and Mann–Whitney tests. But permutation reasoning is not limited to ordinal contexts. Analogous tests can be constructed from the permutation of measured observations—as opposed to rank-transformed observations—and we argue that these tests should often be preferred. Permutation tests can also be used with multiple treatments, with ordered hypothesized effects, and with complex data-structures, such as hypothesis testing in the presence of nuisance variables. Drawing examples from the experimental economics literature, we illustrate how permutation testing solves common challenges. Our aim is to help experimenters move beyond the handful of overused tests in play today and to instead see permutation testing as a flexible framework for statistical inference.
Journal Article
What is considered deception in experimental economics?
In experimental economics there is a norm against using deception. But precisely what constitutes deception is unclear. While there is a consensus view that providing false information is not permitted, there are also “gray areas” with respect to practices that omit information or are misleading without an explicit lie being told. In this paper, we report the results of a large survey among experimental economists and students concerning various specific gray areas. We find that there is substantial heterogeneity across respondent choices. The data indicate a perception that costs and benefits matter, so that such practices might in fact be appropriate when the topic is important and there is no other way to gather data. Compared to researchers, students have different attitudes about some of the methods in the specific scenarios that we ask about. Few students express awareness of the no-deception policy at their schools. We also briefly discuss some potential alternatives to “gray-area” deception, primarily based on suggestions offered by respondents.
Journal Article
Sustaining cooperation in laboratory public goods experiments: a selective survey of the literature
I survey the literature post Ledyard (Handbook of Experimental Economics, ed. by J. Kagel, A. Roth, Chap. 2, Princeton, Princeton University Press, 1995) on three related issues in linear public goods experiments: (1) conditional cooperation; (2) the role of costly monetary punishments in sustaining cooperation and (3) the sustenance of cooperation via means other than such punishments. Many participants in laboratory public goods experiments are “conditional cooperators” whose contributions to the public good are positively correlated with their beliefs about the average group contribution. Conditional cooperators are often able to sustain high contributions to the public good through costly monetary punishment of free-riders but also by other mechanisms such as expressions of disapproval, advice giving and assortative matching.
Journal Article
A penny for your thoughts: a survey of methods for eliciting beliefs
Incentivized methods for eliciting subjective probabilities in economic experiments present the subject with risky choices that encourage truthful reporting. We discuss the most prominent elicitation methods and their underlying assumptions, provide theoretical comparisons and give a new justification for the quadratic scoring rule. On the empirical side, we survey the performance of these elicitation methods in actual experiments, considering also practical issues of implementation such as order effects, hedging, and different ways of presenting probabilities and payment schemes to experimental subjects. We end with a discussion of the trade-offs involved in using incentives for belief elicitation and some guidelines for implementation.
Journal Article
The BCD of response time analysis in experimental economics
For decisions in the wild, time is of the essence. Available decision time is often cut short through natural or artificial constraints, or is impinged upon by the opportunity cost of time. Experimental economists have only recently begun to conduct experiments with time constraints and to analyze response time (RT) data, in contrast to experimental psychologists. RT analysis has proven valuable for the identification of individual and strategic decision processes including identification of social preferences in the latter case, model comparison/selection, and the investigation of heuristics that combine speed and performance by exploiting environmental regularities. Here we focus on the benefits, challenges, and desiderata of RT analysis in strategic decision making. We argue that unlocking the potential of RT analysis requires the adoption of process-based models instead of outcome-based models, and discuss how RT in the wild can be captured by time-constrained experiments in the lab. We conclude that RT analysis holds considerable potential for experimental economics, deserves greater attention as a methodological tool, and promises important insights on strategic decision making in naturally occurring environments.
Journal Article