A Field Experiment on Dynamic Norms

Last registered on May 06, 2022


Trial Information

General Information

A Field Experiment on Dynamic Norms
Initial registration date
September 03, 2021

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
September 03, 2021, 5:33 PM EDT

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

Last updated
May 06, 2022, 10:55 PM EDT

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



Primary Investigator

University of Bochum

Other Primary Investigator(s)

PI Affiliation
University of Cologne

Additional Trial Information

On going
Start date
End date
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Field experiment designed to investigate the role of dynamic norms for donation decisions in a natural setting. In collaboration with a large charitable organization, we test the hypothesis that giving social information by making dynamic norms salient has the potential to increase charitable giving. In a regular donation upgrade request, we vary whether and how information about how many others have increased their donations in the past already. At the same time, we vary whether subjects can choose between relatively low or relatively high upgrade options.

In a second trail, we conduct a two further experiments to better understand effects that we observed in the first trail. The experiments are in particular concerned with the effects of the ask string.
External Link(s)

Registration Citation

Feldhaus, Christoph and Christoph Oslislo. 2022. "A Field Experiment on Dynamic Norms." AEA RCT Registry. May 06. https://doi.org/10.1257/rct.8180-2.0
Experimental Details


Intervention Start Date
Intervention End Date

Primary Outcomes

Primary Outcomes (end points)
Key variables of interest are
- donation decisions at the extensive margin,
- donation sizes (mean, median and modal) and
- total donations raised (also considering cancellation rates)
Primary Outcomes (explanation)
The respective effects of the different treatment conditions are further evaluated at different points in time:
1. immediately after the intervention,
2. after a one-off donation request before Christmas, and
3. in 2022 to investigate long-term effects.

Donation sizes: Subjects can choose between 3€, 5€, 8€ / 8 €, 10€, 13€ or use a write in category.

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
We vary the social information about how many others already updated their regular donation (no info; static; dynamic info) and the suggested donation sizes.

In a second set of experiments we partly repeat the first experiment and in addition investigate the importance of the order of the ask string (upwards vs downwards) and the presence of 'round' numbers.
Experimental Design Details

Regular donors of the charitable organisation receive a donation upgrade request via postal mailing. All mailings are identical except from (1) the precise wording of the how information about how many others have increased their donations in the past already and (2) the choice set at the bottom of each letter. We implement six different treatments using a 3x2 factorial design.

Social Information:
Subjects in T2 & T3 and T5 & T6 receive information about how many other regular donors have upgraded their donation sizes in the past. Subjects in T1 and T4 receive the same mailing, but without any information about others’ behavior.

Static vs. dynamic norm:
Subjects in T3 and T6 receive information about the dynamic norm, namely how the number of regular donors who have upgraded their donation size in the past has developed over time. Subjects in T2 and T5 receive information about the static norm, namely how many other regular donors have already upgraded at a certain point in time.

Choice Set:
While half of the subjects (T4, T5, T6) receive a choice set with relatively low options (3, 5, 8) the other half of the subjects (T1, T2, T3) can choose between relative large numbers (8, 10, 13).


We analyze our experimental data by means of OLS regressions (accounting for the randomization procedure explained under VI):
• The main dependent variables are given by the key variables of interest
• The main independent variables are dummies for assignment to treatments
• To account for potential imbalances between treatment groups, we control for
o date of subjects’ first donation to the charity,
o regular donation size,
o city size,
o gender,
o whether subjects received an additional e-mail,
o and acquisition channel.
(For all non-binary control variables, we also consider log)

• All of these variables are also used for heterogeneity analyses in which interaction terms of each variable with each of the treatment dummies are coefficients of interest
• Regarding the analysis of overall treatment effects including long-term effects: Because we have multiple observations per subject, we cluster standard errors at the subject level.


1. The effect of social information
Take as dependent variables (i) number of donations, (ii) average donation sizes, and (iii) total donations raised. Regress each of these on a Info dummy. We hypothesize that in all three cases the effects are positive and significant.

2. The effect of the dynamic norm
Take as dependent variables (i) number of donations, (ii) average donation sizes, (iii) total donations raised. Regress each of these on a dynamic norm dummy. We hypothesize that in all three cases the effects are positive and significant. We further hypothesize that the effects are larger in size compared to the effects under 1.

3. The effect of the choice set
Take as dependent variables (i) number of donations, (ii) average donation sizes, (iii) total donations raised. Regress each of these on the Choice Set high (Choice Set low) dummy interacted with a dummy for high donor (low donor). We hypothesize that in all three cases the effects are positive and significant.


General Information
- Mailing date: May 2nd letters sent out - currently (May 7th) collection in progress and no data received
- Total N = 52.367 in two additional experiments
- Focus: Effects of the Ask String

Experiment 1
N = 30.366 („Panel“) - only participants who took part in the previous experiment
o All participants received a low (8-5-3) or a high (13-10-8) Ask String in the previous experiment in 2021
o N in 2021 = 33.686 --> Attrition: 3.320

Treatments in Experiment 1 in 2022; again low vs high:
p1: 8-5-3
p2: 13-10-8

Experiment 2
- N: 22.000 („New“) - only participants who did not take part in the previous experiment

Treatments in Experiment 2 - Order of suggested numbers and location of a round number (10/15):
n1: 7-10-14
n2: 14-10-7
n3: 7-11-15
n4: 15-11-7

Online reminder via eMail in both experiments:
Online Ask String: 10-15-20
Default is 15
Online vs. offline upgrade traceable on individual level

Randomization method:
Experiment 1: random block assignment, stratified by 1) “Ask String last year” and 2) “Upgrade last year: yes/no”
p1: 15.184
p2: 15.182

Experiment 2: complete random assignment with 4 treatment arms
n1: 5.498
n2: 5.499
n3: 5.501
n4: 5.502

Main Hypotheses:
1. High Choice Set (vs. low) - sign. higher amounts (also conditional on giving)
2. Upgrade 2021 (vs. no Upgrade 2021) - higher probability Upgrade 2022
3. Subsample: Small Choice Set 2021 + Upgrade; Small Choice Set 2022 (vs. high) - higher probability Upgrade 2022
4. Pooled 2021 and 2022 - Low/Low Choice Set (vs. Low/High, High/Low, and High/High) - higher upgrade incidence (and higher total sum)

1. Descending Ask String (vs. ascending) - higher upgrades (cond. on giving), higher sums, but lower upgrade incidence
2. Compromise Effect weakened by “5er” (n3 & n4)
3. Steeper string (7-10-14 vs. 8-10-13) - increased compromise effect

Other hypotheses:
1. Subsample: High Choice Set 2022 - High previous donation - higher probability Upgrade 2022
2. Subsample: High Choice Set 2022 - Acquisition via F2F - lower probability and sums 2022
3. Online upgrades (vs. offline upgrades) - higher sums and more choices of the default
Randomization Method
For randomization, we use a block random assignment method. We stratified the sample (n = 33.686) by 7 observable pre-intervention characteristics: city population (3 discrete categories), gender, dummy for activated Email-Opt-In, Number of existing commitments, regular donation size (3 discrete categories), duration of existing commitment (3 discrete categories), and a dummy for whether donor has been acquired via face-to-face fundraising or not. Treatments are then randomly assigned on the individual level by a random number generator with numbers from 1 to 3.
Randomization Unit
Was the treatment clustered?

Experiment Characteristics

Sample size: planned number of clusters
Sample size: planned number of observations
Sample size (or number of clusters) by treatment arms
About 5500
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)

Institutional Review Boards (IRBs)

IRB Name
Institutional Review Board GfeW
IRB Approval Date
IRB Approval Number


Post Trial Information

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Data Publication

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Program Files

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