Who cares who intervenes how?

Last registered on July 23, 2019


Trial Information

General Information

Who cares who intervenes how?
Initial registration date
November 28, 2016

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
November 28, 2016, 4:58 AM EST

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

Last updated
July 23, 2019, 6:11 AM EDT

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



Primary Investigator

University of Hamburg; International Max-Planck Research School on Earth System Modeling

Other Primary Investigator(s)

PI Affiliation
University of Hamburg

Additional Trial Information

Start date
End date
Secondary IDs
We conduct an online experiment with a sample representative of the German internet-using population. We endow participants with Credits they may choose to contribute to a large public good or use for private consumption. Contributions to the large public good are real carbon emission reductions. We conduct a questionnaire prior to and after the experiment.
External Link(s)

Registration Citation

Bruns, Hendrik and Grischa Perino. 2019. "Who cares who intervenes how?." AEA RCT Registry. July 23. https://doi.org/10.1257/rct.1661-4.0
Former Citation
Bruns, Hendrik and Grischa Perino. 2019. "Who cares who intervenes how?." AEA RCT Registry. July 23. https://www.socialscienceregistry.org/trials/1661/history/50506
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Experimental Details


Intervention Start Date
Intervention End Date

Primary Outcomes

Primary Outcomes (end points)
Main outcomes of interest are:
(a) Participants' contribution amounts to the public good, measured in credits (continuous variable).
(b) Participants' answers to four questions from a questionnaire measuring the perceived threat to freedom due to the respective intervention (ordinal scales).
(c) Participants' answers to four questions from a questionnaire measuring the perceived anger due to the respective intervention (ordinal variables).
(d) Participants' answers to 11 questions from a questionnaire measuring state reactance prior to treatment allocation (ordinal scales).
Primary Outcomes (explanation)
From the amount contributed to the public good (a), we construct several other outcome variables: (1) a binary variable that takes the value 1 if the participant contributed a positive amount, zero otherwise; (2) a numeric variable measuring the distance of the respective contribution amount to the default value; (3) a binary variable that takes the value 1 if the participant contributed an amount that is equal to the pre-defined value.
In the case of a two-round design (see Experimental design (hidden) for more information), we also construct (a) as a change-score from the (un-treated) baseline contribution to the treated contribution.
From answers given to the questionnaire measuring the perceived threat to freedom (b), anger (c), and state reactance (d), we respectively construct an (un-weighted) factor-based score by adding up the values of the likert-items for each observations.

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
Each participant will receive an endowment and will then be provided with the possibility to divide this amount between him- or herself and a large public good. More specifically, she or he can decide how much (if any) to pay in order to buy carbon licenses from the European Unit Emissions Trading Scheme, in order to contribute to climate protection. Payments will be donated to the NGO TheCompensators* that buys carbon licenses off the EU ETS and retires them.
Experimental Design Details
Before the actual experiment, participants will answer a questionnaire in which we gather information on trait reactance, environmental preferences, and political orientation (Stage 1). There will be no obvious link between questionnaire and decision experiment. Both stages are separated by a non-trivial amount of time, so that the probability that participants presume a connection is minimized.
The actual experiment consists of ten experimental groups, including a control group (Stage 2). Treatments consist of different ways to affect contributions, introduced by differently characterized sources.
The experiment has a fractional factorial 3x4 design. There are two factors, called source type (ST), and intervention type (IT). ST has three levels (no source (NoS), knowledgeable source (KNO), political source (POL)). IT has four levels (no intervention (NoI), recommendation (REC), default (DEF), restriction (RES)). Two possible combinations of both factors are excluded from the design, i.e. no intervention combined with a knowledgeable, as well as political actor. This results in ten combinations, i.e. experimental groups. The combination of no source and no intervention is referred to as the control group.
Among others, our aim is to test whether participants respond differently to treatments given their respective baseline contributions. Since these would normally be counterfactuals, thusly not observable, we elicit baseline contributions before allocating subjects to treatments. Since, theoretically, eliciting baseline contributions might compromise treatment effects, we test this prior to the actual experiment (Stage 2 Block1): We will conduct two treatments, each in two different versions, prior to the others. With this, we test whether letting participants decide about their contribution two times, i.e. first, uninfluenced (baseline), second, in their respective treatment group, changes contributions in the second round significantly compared to only one (treated) decision. In the two-round design, participants are informed that only one of their decisions will be picked randomly to be realized.
We will do this both for the control group, as well as for the combination of default and knowledgeable actor (DEF-KNO). If contributions in the respective two-round vs. one-round alternatives significantly differ, we will conduct all remaining treatments with only one round. Should there be no significant differences, we will conduct all other treatments with two rounds.
Randomization Method
First, the panel provider selects participants based on an internal rotation system that guarantees not always the same subjects are invited to surveys. Selection is representative of the population.
After collecting observations in stage 1, we shuffle all observations to make sure that subjects that were quickest to submit their survey do not have a higher change to be allocated to either treatment of Stage 2 Block 1, than to treatments of Stage 2 Block 2.
Second, we randomly allocate subjects that participated in the first stage to one of four treatments with permuted block randomization (using the R package randomizeR).
Third, we randomly allocate subjects that participated in the first stage (and did not participate in the first block of the second stage) to one of eight treatments with permuted block randomization (using the R package randomizeR).
In case of sample sizes not dividable by the number of groups (i.e. 4 and 8, respectively for Block 1 and 2 of Stage 2), the number of subjects needed to reduce the overall sample to a number dividable by the block size is allocated to treatments based on complete randomization (using the R package randomizeR).
Randomization Unit
Randomization is on individual level.
Was the treatment clustered?

Experiment Characteristics

Sample size: planned number of clusters
No clustering
Sample size: planned number of observations
We target 960 observations. Assuming an attrition rate of 30% between questionnaire and treatment allocation, ca. 1.370 participants are invited for the questionnaire.
Sample size (or number of clusters) by treatment arms
In the first stage, i.e. before treatments are randomly allocated to participants, 1.370 observations are targeted.
In the second stage, i.e. after randomized allocation to treatment groups took place, 960 observations are targeted. Because 160 observations will be used to decide between the one-round and two-round design, 800 observations will be used for final analysis. This will amount to 80 observations per experimental group.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
The main outcome of interest is the interaction between treatment factors. We calculate the minimum detectable effect size assuming a significance level of 0.05, a power of 0.8, n = 720, using a two-way analysis of variance (ANOVA) excluding the control group, resulting in a 3 x 3 design with 80 observations per group. We do this for the two main effects, as well as for the interaction effect. The resulting effect size eta is the square root of the ratio of the variance explained by the respective factor to the error variance. We calculate the minimum detectable effect size (MDES) of 0.12 assuming an error variance of 1 for main treatment effects, and a MDES of 0.13 for the interaction effect. Further increases of the number of observations per group do not significantly decrease the MDES, with the minimum being slightly below 0.1 for more than 110 observations per group. Note that our analysis will primarily rely on regression models that better fit the distribution of the likely not normally distributed outcome variable of interest, e.g. a (random-effects) Tobit model to account for left-censoring of contributions. This affects power. With respect to pairwise comparisons with t-tests (or non-parametric Mann-Whitney-U tests), assuming an error probability of 0.05, a power of 0.8, n = 80 per group, an arbitrarily assumed mean of 20 Credits in the control group, we vary the common standard deviations between 10 and 20. This results in minimum detectable differences if contributions in the treatment group range between 24.46 and 28.91 Credits, i.e. if there is a difference between 4.46 and 8.91 to the control, linearly increasing with increased pooled variance. Notably, detecting effects of treatments that we hypothesize to decrease contributions with respect to another group may be problematic when contributions in the respective control group are low. This is because contributions are censored at zero. For lower treatment contributions to be detectable with standard deviations up to 20, the respective contributions in the control group must not be lower than 9 Credits. However, in that case average treated contributions would need to be zero in order for the difference to be detectable with this design. Note that in case of conducting the experiment as a within-subject design, power increases, ceteris paribus.
Supporting Documents and Materials

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Institutional Review Boards (IRBs)

IRB Name
IRB Approval Date
IRB Approval Number


Post Trial Information

Study Withdrawal

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Is the intervention completed?
Intervention Completion Date
January 20, 2017, 12:00 +00:00
Data Collection Complete
Data Collection Completion Date
January 19, 2017, 12:00 +00:00
Final Sample Size: Number of Clusters (Unit of Randomization)
806 individuals
Was attrition correlated with treatment status?
Final Sample Size: Total Number of Observations
806 individuals
Final Sample Size (or Number of Clusters) by Treatment Arms
75 individuals control, 90 individuals Rec-Nos, 83 individuals Def-Nos, 86 individuals Res-Nos, 77 individuals Rec-Exp, 73 individuals Def-Exp, 79 individuals Res-Exp, 83 individuals Rec-Pol, 81 individuals Def-Pol, 79 individuals Res-Pol
Data Publication

Data Publication

Is public data available?

Program Files

Program Files
Reports, Papers & Other Materials

Relevant Paper(s)

Reports & Other Materials