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We present simple one-shot distribution experiments comparing the relative importance of efficiency concerns, maximin preferences, and inequality aversion, as well as the relative performance of the fairness theories by Gary E Bolton and Axel Ockenfels and by Ernst Fehr and Klaus M. Schmidt. While the Fehr-Schmidt theory performs better in a direct comparison, this appears to be due to being in line with maximin preferences. More importantly, we find that a combination of efficiency concerns, maximin preferences, and selfishness can rationalize most of the data while the Bolton-Ockenfels and Fehr-Schmidt theories are unable to explain important patterns.

Field survey experiments often measure amorphous concepts in discretely ordered categories, with postsurvey analytics that fail to account for the discrete attributes of the data. This article demonstrates the use of discrete distribution tests, specifically the chi-square test and the discrete Kolmogorov—Smirnov (KS) test, as simple devices for comparing and analyzing ordered responses typically found in surveys. In Monte Carlo simulations, we find the discrete KS test to have more power than the chi-square test when distributions are right or left skewed, regardless of the sample size or the number of alternatives. The discrete KS test has at least as much power as the chi-square, and sometimes more so, when distributions are bi-modal or approximately uniform and samples are small. After deriving rules of usage for the two tests, we implement them in two cases typical of survey analysis. Using our own data collected after Hurricanes Katrina and Rita, we employ our rules to both validate and assess treatment effects in a natural experimental setting.

Explores the question of accuracy in the estimation of opinion distributions, based on results of surveys exploring respondent ([R] N = 808 adult Jews in Israel) estimates of public opinion on 24 issues. Results indicate that people are not very good in their exact numerical estimates, but they do much better in identifying the majority opinion on an issue. The role of motivation for accuracy is also examined through a survey experiment (N = 202 Rs). Findings suggest that the Rs tried harder when motivated, but did not improve their estimates, neither of the exact distribution nor of the majority opinion. 3 Tables, 45 References. Adapted from the source document.

Background: Random design experiments are a powerful device for estimating average treatment effects, but evaluators sometimes seek to estimate the distribution of treatment effects. For example, an evaluator might seek to learn the proportion of treated units who benefit from treatment, the proportion who receive no benefit, and the proportion who are harmed by treatment. Method: Imbens and Rubin (I&R) recommend a Bayesian approach to drawing inferences about the distribution of treatment effects. Drawing on the I&R recommendations, this article explains the approach; provides computing algorithms for continuous, binary, ordered and countable outcomes; and offers simulated and real-world illustrations. Results: This article shows how the I&R approach leads to bounded uncertainty intervals for summary measures of the distribution of treatment effects. It clarifies the nature of those bounds and shows that they are typically informative. Conclusions: Despite identification issues, bounded solutions provide useful insight into the distribution of treatment effects. We recommend that evaluators incorporate analyses of the distribution of treatment effects into new studies and that evaluators revisit completed studies to estimate the distribution of treatment effects.

Rod bundle experiments with axially uniform and nonuniform heat fluxes are examined to explore the potential limitations of using uniform rod bundle CHF data for CHF correlation development of light water reactors with nonuniform axial power distribution (APD). The case of upstream burnout is presented as an example of unique phenomena associated with nonuniform rod bundle CHF experiments. It is a result from combined effect of axial nonuniform power shape and different interchannel mixing mechanisms. In addition, several key parameters are investigated with respect to their potential impacts on the thermal-hydraulic behaviors between rod bundles with uniform and nonuniform APDs. This type of misrepresentation cannot be amended or compensated through the use of correction factors due to the lack of critical information in the uniform rod bundle CHF testing as well as the fundamental difference in the underlining driving mechanisms. Other potential issues involved with the use of uniform rod bundle CHF data for nonuniform APD system applications also present strong evidence concerning the limitations and inadequacy of using uniform rod bundle CHF data for the correlation, prediction, and design limit calculation for safety analysis.