Going beyond the in-/out-group dichotomy: Investigating altruism towards middle-groups

Last registered on July 19, 2022


Trial Information

General Information

Going beyond the in-/out-group dichotomy: Investigating altruism towards middle-groups
Initial registration date
June 28, 2022

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
July 08, 2022, 12:15 PM EDT

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

Last updated
July 19, 2022, 3:34 AM EDT

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



Primary Investigator

Heidelberg University

Other Primary Investigator(s)

Additional Trial Information

In development
Start date
End date
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
In-group favouritism and out-group hostility are well-known phenomena in research, which are repeatedly documented across fields, contexts and methods. But the world around us does not always fit into this dichotomy of in- and out-group. Often there is a "middle-group" that shares some identity characteristics with the in-group, but ultimately neither belongs to the in-group, nor is an alien out-group. This setting raises the question how altruistic actions towards a middle-group compare to actions in favour of an in-group or against an out-group.

Using an online laboratory experiment, we investigate altruistic actions in an allocation task with the minimal group paradigm. Through a between-subject design we causally identify the change in altruistic actions when a middle group is introduced into the two-group setting of in- and out-group. Furthermore, we vary the identity characteristics of the middle-group which allows us to measure the impact of characteristics and arrange the types of middle-groups on a scale.

With this paper we extend the current research on in- and out-groups conceptually and our results help to model dynamics behind altruistic actions towards different groups, for example refugee groups with diverse backgrounds.
External Link(s)

Registration Citation

Houf, Leon. 2022. "Going beyond the in-/out-group dichotomy: Investigating altruism towards middle-groups." AEA RCT Registry. July 19. https://doi.org/10.1257/rct.9670
Experimental Details


Intervention Start Date
Intervention End Date

Primary Outcomes

Primary Outcomes (end points)
1) The success in the group task:
Has a group solved the group-task successfully: yes/no? We will regress this binary outcome with a probit regression on the group-size and the chosen artist. H0: group-size and chosen artist have no statistically significant effect on the success in the group-task.

2) Allocations of coins to the different cups:
First, as an overview of the results we calculate the mean allocation x of all participants to the respective cups per treatment and per part.

3) Delta in allocation of coins to theoretical value:
We then divide this mean allocation of coins by how many coins should theoretically be put into that cup (15, if there are two cups in a part – 10 if there are three cups): x/10 respective y/15. These values can be interpreted as “for every coin that should have been put into this cup, how many coins were actually put into it”

4) Causal effect of introducing a middle-group:
We will use a difference-in-difference approach to identify the causal effect of introducing a middle-group. The control group is the “2-2” group, the treatment group the “2-3” group. The first part is the pre-treatment, the second part is the post-treatment period. We will look at two outcome measures correcting for multiple hypothesis testing: (i) When a middle-group is introduced, what is the effect on the delta in allocation of coins compared to the theoretical value for the in-group? (ii) … for the out-group? H0: there are no effects.

5) Compare this effect between the alternative middle-groups:
Treatments “2-3” and “2-3a” use two different types of middle-groups. Everything we do for the “2-3” treatment in the last part (4) of the analysis we will also do for treatment “2-3a” and then compare the coefficients and their standard deviations.

6) Effect of having had a middle-group from the beginning versus being introduced to it later:
The treatment “3-3” had the middle-group from the start, the treatment “2-3” only in the second part. To investigate the effect of this difference in experience with a middle-group, we will pool all data from the second round of these to treatments and run an OLS regression with the treatment-variable (i.e. the difference in experience) as explanatory variable. We will do this for three outcome measures correcting for multiple hypothesis testing: (i) What is the effect of being introduced to a middle-group already at the start on the delta in allocation of coins compared to the theoretical value for the in-group? (ii) … for the middle-group? (iii) … for the out-group? H0: There are no effects.

7) Analysis on the individual level:
For each participant and part we will count how many coins were put into cup x and calculate the “delta in absolute coins” by subtracting the theoretical amount of coins (10 or 15). We will plot the “delta in absolute coins per cup”-distribution of all participants in each treatment, part and cup on a x-scale [-15, +15]. We will then perform a Monte-Carlo-Simulations for each of these plots where the simulated agents indeed distribute the coins randomly as specified by the rules. The difference to the actual experimental data is that there the subjects might in fact not have followed the rules. In this case we will observe different distributions. Then for each treatment, part and cup we compare the simulated results with the actual results of the human experimental participants with a Kolmogorov-Smirnov-test.
H0: The distributions of the results of the experimental data and the simulated data are statistically not different from each other.

8) Behaviour over time:
Per treatment and round we calculate the mean contribution per cup. First, we will plot this variable over all rounds. Then we will perform an OLS regression on this variable with rounds as explanatory variable to see whether there are time trends. H0: there are no time trends.

Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
The main feature of the present study is the introduction of “middle-groups” between in- and out-groups within the minimal group paradigm.
First, participants are asked whether they prefer a painting by Kandinsky or Klee. Based on this choice participants are grouped in groups of 3-5 where everyone chose the same painting. This is the in-group of a participant. An out-group member is described as a random participant who takes part in this study at another (unknown to the participant) university, at a different time and who chose the other painting. A member of a middle-group is described as a random participant who takes part in this study at the same university at a different time who chose the same painting. In a variation that participant is at another university.
With their respective in-group, the participants have to solve a group task. Individually they have to identify which of two paintings was made by the painter they chose in the first activity, there are five rounds of this with new paintings in each round. All the individual guesses in a group are added up, and if a group made more correct than false guesses they win a monetary reward.
As task, the experiment uses an adaption of the mind game as presented in Hruschka et al (2014) ‘Impartial Institutions, Pathogen Stress and the Expanding Social Network’. Participants are presented cups that are associated with the groups mentioned above (in-, middle-, alternative middle-, out-group). In step 1 of every round they should pick one of the cups in their head (unobservable to the experimenter), in step 2 they should roll a die (unobservable to the experimenter), in step 3 they should put 1 coin in one of the cups depending on the cup they chose in their head and the result of the die-roll. Whether participants put the coin into the “correct” cup is unobservable to the experimenter. But due to the random nature of the die-roll, it should be equal in all cups, which can be tested statistically.
The experiment consists of two parts directly after each other with 30 rounds of the above-mentioned steps in both parts, so in total 60 rounds.
There are three setups of the cups:
• In the first setup (“2”), there are only cups of the in- and out-group.
• In the second setup (“3”), there are cups of in-, middle- and out-group.
• In the third setup (“3a”), there are cups of in-, the above-mentioned alternative middle- and out-group.
We use four treatments that combine the setups in the two parts in different ways: (“first part” – “second part”):
1. 2-2
2. 2-3
3. 3-3
4. 2-3a
We plan to run this study with 4 sessions each at Heidelberg University and at Hochschule Rhein-Waal. In each session we aim to have 30-35 participants, so a total sample size of 240-280. The treatments will be randomly assigned, so will be roughly, but most likely not exactly, similar in size.
Experimental Design Details
Randomization Method
Randomization by computer (oTree / python)
Randomization Unit
1. individuals to groups 2. groups to treatments
Was the treatment clustered?

Experiment Characteristics

Sample size: planned number of clusters
2 different universities
Sample size: planned number of observations
240-280 university students
Sample size (or number of clusters) by treatment arms
60-70 per treatment, 120-140 per universities, where treatments are roughly equally split over universities
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)

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?
Data Collection Complete
Data Publication

Data Publication

Is public data available?

Program Files

Program Files
Reports, Papers & Other Materials

Relevant Paper(s)

Reports & Other Materials