In exploring deliberative dynamics within mini-publics, it has been observed that initial group-building activities play a crucial role in enhancing deliberative reasoning. However, the influence of liberal democratic practices such as voting mechanisms and the inclusion of strategic or representative stakeholders, on deliberative processes is not well understood. This study undertakes a comparative configurational meta-analysis (CCMA) of 22 minipublics to investigate how these liberal democratic elements influence deliberative reasoning. Results indicate that participants’ deliberative reasoning is significantly enhanced in contexts where initial group activities are coupled with prolonged periods of deliberation and where voting is minimised or absent. In contrast, the presence of voting mechanisms, strategic stakeholder involvement, and a high impact of minipublics on decision-making processes are associated with weaker, negative, or stable participant deliberative reasoning. These findings contribute to the broader discourse on the integration of deliberative and non-deliberative components within minipublics, highlighting the potential negative impact of strategic behaviour on the quality of deliberation.

We report two meta-analyses on the determinants of antisocial behavior in experimental settings in which such behavior is not rationally motivated by pecuniary incentives. The first meta-analysis employs aggregate data from 95 published and unpublished studies (24,086 participants), using laboratory, field and online experiments carried out since 2000. We find that antisocial behavior depends significantly on the experimental setting, being highest in vendetta games and lowest in social dilemmas. As we find significant heterogeneity across the studies, including across game classes, in the second meta-analysis, we focus only on “Joy of Destruction” (JoD) and money-burning (MB) experiments, for which we have the most observations, 51 studies and around 16,784 participants. Overall, our findings suggest that procedural fairness and being observed by others reduce the frequency of antisocial behavior. Online and field experiments display more antisocial behavior than laboratory experiments. We also find that the strategy method biases antisocial behavior upward. However, we do not find evidence for a positive publication bias being correlated with higher destructive behavior, either in the general meta-analysis or in relation to JoD/MB experiments; if anything, there is evidence of a negative publication bias. The JoD/MB meta-analysis finds evidence of a price effect for destruction frequency, negative discrimination against outsiders, within-subject designs underestimating destructive behavior, and more antisocial behavior in one-shot interactions. Collectively, our results point to the value of more laboratory experiments that systematically build on paradigmatic experimental designs to enable comparability and the identification of key economic drivers of antisocial behavior.

A fundamental pillar of science is the estimation of the effect size of associations. However, this task is sometimes difficult and error-prone. To facilitate this process, the R package metaConvert automatically calculates and flexibly converts multiple effect size measures. It applies more than 120 formulas to convert any relevant input data into Cohen’s d, Hedges’ g, mean difference, odds ratio, risk ratio, incidence rate ratio, correlation coefficient, Fisher’s r-to-z transformed correlation coefficient, variability ratio, coefficient of variation ratio, or number needed to treat. Researchers unfamiliar with R can use this software through a browser-based graphical interface (https://metaconvert.org/). We hope this suite will help researchers in the life sciences and other disciplines estimate and convert effect sizes more easily and accurately.

Conventional meta-analyses (both fixed and random effects) of correlations are biased due to the mechanical relationship between the estimated correlation and its standard error. Simulations that are closely calibrated to match actual research conditions widely seen across correlational studies in psychology corroborate these biases and suggest two solutions: UWLS+3 and HS. UWLS+3 is a simple inverse-variance weighted average (the unrestricted weighted least squares) that adjusts the degrees of freedom and thereby reduces small-sample bias to scientific negligibility. UWLS+3 as well as the Hunter and Schmidt approach (HS) are less biased than conventional random-effects estimates of correlations and Fisher’s z, whether or not there is publication selection bias. However, publication selection bias remains a ubiquitous source of bias and false-positive findings. Despite the relationship between the estimated correlation and its standard error in the absence of selective reporting, the precision-effect test/precision-effect estimate with standard error (PET-PEESE) nearly eradicates publication selection bias. Surprisingly, PET-PEESE keeps the rate of false positives (i.e., type I errors) within their nominal levels under the typical conditions widely seen across psychological research whether there is publication selection bias, or not.

Double-zero-event studies (DZS) pose a challenge for accurately estimating the overall treatment effect in meta-analysis (MA). Current approaches, such as continuity correction or omission of DZS, are commonly employed, yet these ad hoc methods can yield biased conclusions. Although the standard bivariate generalized linear mixed model (BGLMM) can accommodate DZS, it fails to address the potential systemic differences between DZS and other studies. In this article, we propose a zero-inflated bivariate generalized linear mixed model (ZIBGLMM) to tackle this issue. This two-component finite mixture model includes zero inflation for a subpopulation with negligible or extremely low risk. We develop both frequentist and Bayesian versions of ZIBGLMM and examine its performance in estimating risk ratios against the BGLMM and conventional two-stage MA that excludes DZS. Through extensive simulation studies and real-world MA case studies, we demonstrate that ZIBGLMM outperforms the BGLMM and conventional two-stage MA that excludes DZS in estimating the true effect size with substantially less bias and comparable coverage probability.

Network meta-analysis (NMA), also known as mixed treatment comparison meta-analysis or multiple treatments meta-analysis, extends conventional pairwise meta-analysis by simultaneously synthesizing multiple interventions in a single integrated analysis. Despite the growing popularity of NMA within comparative effectiveness research, it comes with potential challenges. For example, within-study correlations among treatment comparisons are rarely reported in the published literature. Yet, these correlations are pivotal for valid statistical inference. As demonstrated in earlier studies, ignoring these correlations can inflate mean squared errors of the resulting point estimates and lead to inaccurate standard error estimates. This article introduces a composite likelihood-based approach that ensures accurate statistical inference without requiring knowledge of the within-study correlations. The proposed method is computationally robust and efficient, with substantially reduced computational time compared to the state-of-the-science methods implemented in R packages. The proposed method was evaluated through extensive simulations and applied to two important applications including an NMA comparing interventions for primary open-angle glaucoma, and another comparing treatments for chronic prostatitis and chronic pelvic pain syndrome.

Substantial productivity increases have been reported when incentives are framed as losses rather than gains. Loss-framed contracts have also been reported to be preferred by workers. The results from our meta-analysis and real-effort experiment challenge these claims. The meta-analysis’ summary effect size of loss framing is a 0.16 SD increase in productivity. Whereas the summary effect size in laboratory experiments is a 0.33 SD, the summary effect size from field experiments is 0.02 SD. We detect evidence of publication biases among laboratory experiments. In a new laboratory experiment that addresses prior design weaknesses, we estimate an effect size of 0.12 SD. This result, in combination with the meta-analysis, suggests that the difference between the effect size estimates in laboratory and field experiments does not stem from the limited external validity of laboratory experiments, but may instead stem from a mix of underpowered laboratory designs and publication biases. Moreover, in our experiment, most workers preferred the gain-framed contract and the increase in average productivity is only detectable in the subgroup of workers (~ 20%) who preferred the loss-framed contracts. Based on the results from our experiment and meta-analysis, we believe that behavioral scientists should better assess preferences for loss-framed contracts and the magnitude of their effects on productivity before advocating for greater use of such contracts among private and public sector actors.

This paper reports the findings of a meta-analysis of 37 papers with 75 results from ultimatum game experiments. We find that on average the proposer offers 40% of the pie to the responder. This share is smaller for larger pie sizes and larger when a strategy method is used or when subjects are inexperienced. On average 16% of the offers is rejected. The rejection rate is lower for larger pie sizes and for larger shares offered. Responders are less willing to accept an offer when the strategy method is employed. As the results come from different countries, meta-analysis provides an alternative way to investigate whether bargaining behavior in ultimatum games differs across countries. We find differences in behavior of responders (and not of proposers) across geographical regions. With one exception, these differences cannot be attributed to various cultural traits on which for instance the cultural classifications of Hofstede (1991) and Inglehart (2000) are based.

We collect individual participant data from 70 papers that use laboratory experiments to examine individual tax evasion behavior (or “Tax Evasion Games”), in order to use meta-analysis to estimate the impacts of different public policy, experimental design and individual level variables on tax evasion choices. Our results show that standard enforcement variables like audits (including audit rules) and fines perform differently on the extensive and intensive margins. We find that other fiscal variables like a flat tax system, tax rates, and tax amnesties have unambiguous negative impacts on tax compliance, and that specific features of the experimental setting, such as how subjects are directed to report income, or whether taxes are redistributed to the participants or to a real life public good, have significant impacts on tax compliance. Our results also indicate that the demographic characteristics of the subjects (e.g., gender, experimental income, occupation, risk attitude) affect compliance.

Objective: To use meta-analysis techniques to assess the impact of various factors on the extent of cooperation in standard linear public goods experiments using the voluntary contributions mechanism.

Data Sources: Potentially relevant experiments were identified through searches of EconLit, the Internet Documents in Economics Access Service (IDEAS), and a survey article.

Review Methods: A total of 349 potentially relevant studies were identified. Of these, 27 (representing a total of 711 groups of participants) met the inclusion criteria. Data were abstracted from these studies using a standardized protocol. Results were analyzed using weighted ordinary least squares. Average group efficiency was the dependent variable.

Results: The marginal per capita return, communication, constant group composition over the session (“partners”), positive framing, and the use of children as subjects had a positive and significant effect (p < 0.05) on the average level of contribution to the public good. Heterogeneous endowments to subjects, experienced participants, and soliciting subjects’ beliefs regarding other participants’ behaviour prior to the start of the session/period had a negative and significant effect. A number of other factors were not identified as significant.

Conclusion: The meta-analysis results parallel several key findings from previous literature reviews. In addition, they offer parameter estimates and an analysis of significance based on the totality of the available research evidence. More consistent reporting of the results of experiments would greatly improve the ability to conduct this type of research.

One important determinant of voluntary contributions to public goods is the value of the public good relative to that of the forgone private good. Isaac, Walker and Thomas (1984) formalized this relation in the Marginal Per Capita Return (MPCR) and demonstrated its influence on the provision of linear public goods. This paper develops a parallel concept, in the context of a threshold public good, the Step Return (SR). After providing a meta-analysis of the effect of SR in previous experiments, we compare contributions in threshold public goods games with low, medium and high SRs. Results show that subjects respond to the SR in this setting just as they respond to the MPCR in the linear public goods setting: higher SRs lead to more contributions.

In this paper, we report a replication of Engel’s (Exp. Econ. 14(4):583–610, 2011) meta-study of dictator game experiments. We find Engel’s meta-study of dictator game experiments to be robust, with one important exception: the coding of the take-option (List in J. Polit. Econ. 115(3):482–493, 2007; Bardsley in Exp. Econ. 11(2):122–133, 2008; Cappelen et al. in Econ. Lett. 118(2):280–283, 2013). While Engel reports this as having no statistically significant effect, in our replications, we find an economically and statistically significant negative effect on giving in line with the relevant literature.

A key parameter estimated by lab and field experiments in economics is the individual discount rate—and the results vary widely. We examine the extent to which this variance can be attributed to observable differences in methods, subject pools, and potential publication bias. To address the model uncertainty inherent to such an exercise we employ Bayesian and frequentist model averaging. We obtain evidence consistent with publication bias against unintuitive results. The corrected mean annual discount rate is 0.33. Our findings also suggest that discount rates are independent across domains: people tend to be less patient when health is at stake compared to money. Negative framing is associated with more patience. Finally, the results of lab and field experiments differ systematically, and it also matters whether the experiment relies on students or uses broader samples of the population.