Cost-effective public good provision under consequential uncertainty

Last registered on November 02, 2023

View Trial History

Pre-Trial

Trial Information

General Information

Title

Cost-effective public good provision under consequential uncertainty

RCT ID

AEARCTR-0006304

Initial registration date

August 17, 2020

Initial registration date is when the trial was registered.

It corresponds to when the registration was submitted to the Registry to be reviewed for publication.

First published

August 18, 2020, 11:00 AM EDT

First published corresponds to when the trial was first made public on the Registry after being reviewed.

Last updated

November 02, 2023, 8:39 PM EDT

Last updated is the most recent time when changes to the trial's registration were published.

Locations

Country

New Zealand

Region

Primary Investigator

Name

Ronald Peeters

Affiliation

University of Otago

Contact Primary Investigator

Other Primary Investigator(s)

PI Name

Nathan Chan

PI Affiliation

University of Massachusetts Amherst

PI Name

Stephen Knowles

PI Affiliation

Univesity of Otago

PI Name

Leonard Wolk

PI Affiliation

Free University Amsterdam

Additional Trial Information

Status

Completed

Start date

2020-04-14

End date

2023-04-24

Keywords

Other

Additional Keywords

group decision making, public good games, cooperation, risk and ambiguity

JEL code(s)

C72, C92, D64, H41

Secondary IDs

Prior work

This trial does not extend or rely on any prior RCTs.

Abstract

When individuals are exposed to multiple public goods at once, that may differ in investment costs and potential benefit, there is the possibility to contribute in a cost-ineffective manner: investments in one public good could better have been invested in another public good. Within this project we study how cost-effectiveness of investment behaviour relates to the nature of the uncertainty underlying the costs and benefits of investments, and which behavioural factors (i.e. individual preferences/attitudes/cognition) are predictive for cost-ineffective giving in which environmental circumstances.

External Link(s)

Registration Citation

Citation

Chan, Nathan et al. 2023. "Cost-effective public good provision under consequential uncertainty." AEA RCT Registry. November 02. https://doi.org/10.1257/rct.6304-1.1

Sponsors & Partners

Experimental Details

Interventions

Intervention(s)

Intervention (Hidden)

Intervention Start Date

2020-08-18

Intervention End Date

2023-04-24

Primary Outcomes

Primary Outcomes (end points)

Our primary outcome is whether uncertainty of consequences in public goods affect cost-effectiveness of individual contributions.

Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)

How does cost-effectiveness of contributions relate to individual attitudes towards risk and ambiguity, and giving-type?

Secondary Outcomes (explanation)

Experimental Design

The experiment will be conducted online and participants will be recruited using Prolific. Participants are operating within groups of size three in which they have to solve three tasks (treatments). In each task they have to decide how much to contribute to each of four public goods. In the first task the marginal per capita returns (MPCR) of each of the public goods are known (“certainty”); in the second task the MPCRs are subject to known uncertainty (“risk”); and in the third task the MPCRs are subject to unknown uncertainty (“ambiguity”). Participants will be rewarded according to one of the tasks, but according to all four public goods within this task. The MPCRs are chosen such that cost-effectiveness can be unambiguously determined for all treatments, independent of risk/ambiguity attitude and of individuals’ beliefs about contributions by others. After having solved these tasks, there will be a short questionnaire with some demographic questions (gender and age), an elicitation of their giving-type, their risk and ambiguity attitude, and their attention level and risk literacy. Participants record all their decisions in the computer software (programmed in oTree).

Experimental Design Details

Randomization Method

All randomizations are computerized

Randomization Unit

Which task is rewarded and the MPCRs are randomized on group level. Randomizations in the questionnaire (to elicit risk and ambiguity attitude) are on individual level.

Was the treatment clustered?

Yes

Experiment Characteristics

Sample size: planned number of clusters

Different groups will visit the tasks (treatments) in a different order. These orders can be considered clusters. We have six of them.

Sample size: planned number of observations

We aim for 216 observations (i.e. participants), in order to secure 180 observations. We had 201 participants. During the peer-review process we have been asked to collect more observations, in response to which we collected 107 more observations when aiming for 108.

Sample size (or number of clusters) by treatment arms

We aim for equal distribution of observations across the six clusters.

Minimum detectable effect size for main outcomes (accounting for sample design and clustering)

Supporting Documents and Materials

IRB