The voluntary contribution mechanism with complementarity

This dissertation investigates the effect of complementarity in the private provision of public goods. The inclusion of complementarity in contributions transforms the classic voluntary contribution mechanism into a coordination game, in which we are able to study how different behavioral types interact. The first chapter generalizes the linear voluntary contribution mechanism case by allowing agents' contributions to be complements in production. When complementarity is sufficiently high, an additional full-contribution equilibrium emerges. We experimentally investigate subjects' behavior using a between-subject design that varies complementarity. When two equilibria exist, subjects tend to coordinate on contributions close to the efficient equilibrium. When complementarity is sizable but only a zero-contribution selfish-equilibrium exists, subjects persistently contribute above it. Observed choices and other nonchoice data indicate heterogeneity among subjects and two distinct types. Homo pecuniarius maximizes profits by best responding to beliefs, while Homo behavioralis identifies this strategy but chooses to deviate from it, sacrificing pecuniary rewards to support altruism or competitiveness. The second chapter studies the effect of introducing thresholds on equilibrium selection in the voluntary contribution mechanism with complementarity (VCMC). The introduction of thresholds expands the basin of attraction of the socially inefficient equilibrium by raising the minimum group output necessary for a positive return from cooperation. If contributions are not sufficient to generate the minimum output, the social return is zero. The data suggest that the introduction of thresholds does not alter the equilibrium selection or the pattern of contribution dynamics. Individuals are still able to coordinate on the socially preferable outcome. The third chapter examines experimentally the effect of displaying feedback about the income of other group members on the evolution of contributions in the VCMC. The results show that the degree of coordination responds to the way information about outcomes is made available to subjects. Emphasis on income differences can be detrimental from a social perspective and may result in the unraveling of cooperation.Arts, Faculty ofVancouver School of EconomicsGraduat

Assessing Robustness to Varying Clustering Methods and Samples in Ambuehl, Bernheim, and Lusardi (2022): Replication and Sensitivity Analysis

Ambuehl et al. (2022) explore ways to evaluate interventions designed to enhance decision-making quality when individuals misjudge the outcomes of their choices. The authors propose a novel outcome metric that can distinguish between interventions better than conventional metrics such as financial literacy and directional behavioral responses. The proposed metric, which transforms price-metric bias into interpretable welfare loss measures, can be applied to evaluate various training programs on financial products. Table 4 of the paper reports the authors' significant main point estimates at the 1% level. In this replication exercise, we first replicate the main findings of the original paper. Then, we modify the clustering method by using k-means with demographic variables as inputs, then we re-calculate standard errors with jackknife estimators. Finally, we include subjects who were excluded by the authors due to multiple switching in the multiple price lists. We find that all of these replications result in robust findings. Additionally, we successfully replicate Figure 4 from the paper. Notably, this replication demonstrates the insensitivity of the results to the choice of distance metric

Mass Reproducibility and Replicability: A New Hope

This study pushes our understanding of research reliability by reproducing and replicating claims from 110 papers in leading economic and political science journals. The analysis involves computational reproducibility checks and robustness assessments. It reveals several patterns. First, we uncover a high rate of fully computationally reproducible results (over 85%). Second, excluding minor issues like missing packages or broken pathways, we uncover coding errors for about 25% of studies, with some studies containing multiple errors. Third, we test the robustness of the results to 5,511 re-analyses. We find a robustness reproducibility of about 70%. Robustness reproducibility rates are relatively higher for re-analyses that introduce new data and lower for re-analyses that change the sample or the definition of the dependent variable. Fourth, 52% of re-analysis effect size estimates are smaller than the original published estimates and the average statistical significance of a re-analysis is 77% of the original. Lastly, we rely on six teams of researchers working independently to answer eight additional research questions on the determinants of robustness reproducibility. Most teams find a negative relationship between replicators' experience and reproducibility, while finding no relationship between reproducibility and the provision of intermediate or even raw data combined with the necessary cleaning codes