The conditional contribution mechanism for repeated public goods - The general case [Data & Replication package]

DOI

We present a new and simple mechanism for repeated public good environments. In the Conditional Contribution Mechanism (CCM), agents send two messages of the form, “I am willing to contribute x units to the public good if in total y units are contributed.” This mechanism offers agents risk-free strategies, which we call unexploitable. Our main theorem states that all outcomes of the CCM will eventually be Pareto efficient if agents choose unexploitable better responses. We conduct a laboratory experiment to investigate whether observed behavior is consistent with this prediction. In the complete information case we find that indeed almost 80% of outcomes are Pareto optimal. Furthermore, in comparison to the Voluntary Contribution Mechanism, the CCM leads to significantly higher contribution rates. Even under incomplete information, contributions are fairly high and do not deteriorate over time, although surprisingly the VCM does equally well in this case. Theoretically, allowing agents to send two messages helps them ensure the status-quo and at the same time offer higher contribution levels. In a further treatment, where we allow agents to send only one message, we find that total contributions are consistently lower than the CCM in early periods but are similar in later periods.

Identifier
DOI https://doi.org/10.11588/data/ZPTF1X
Related Identifier https://doi.org/10.1016/j.jet.2022.105488
Metadata Access https://heidata.uni-heidelberg.de/oai?verb=GetRecord&metadataPrefix=oai_datacite&identifier=doi:10.11588/data/ZPTF1X
Provenance
Creator Oechssler, Jörg ORCID logo; Reischmann, Andreas; Sofianos, Andis
Publisher heiDATA
Contributor Oechssler, Jörg; heiDATA: Heidelberg Research Data Repository
Publication Year 2022
Rights ODC-By v1.0; info:eu-repo/semantics/openAccess; https://opendatacommons.org/licenses/by/1-0/
OpenAccess true
Contact Oechssler, Jörg (Alfred-Weber-Institute of Economics, Heidelberg University, Heidelberg, Germany)
Representation
Resource Type Dataset
Format application/x-stata-syntax; application/zip
Size 43190; 14796; 30524600; 6961823
Version 1.2
Discipline Agriculture, Forestry, Horticulture, Aquaculture; Agriculture, Forestry, Horticulture, Aquaculture and Veterinary Medicine; Design; Fine Arts, Music, Theatre and Media Studies; Humanities; Life Sciences; Social Sciences; Social and Behavioural Sciences; Soil Sciences