Public Good Provision in Overlapping Neighborhoods

Last registered on January 23, 2023


Trial Information

General Information

Public Good Provision in Overlapping Neighborhoods
Initial registration date
January 18, 2023

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
January 23, 2023, 7:02 AM EST

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


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Primary Investigator

University of Hamburg

Other Primary Investigator(s)

PI Affiliation
University of Hamburg
PI Affiliation
University of Hamburg

Additional Trial Information

In development
Start date
End date
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
This project studies the voluntary provision of a weakest-link public good in overlapping neighborhoods. Located around a virtual table, each individual’s investment decision affects the provision of the public good of herself and her right- and left-hand neighbors. We hypothesize that the introduction of this spatial element can provide obstacles to the level at which the public good is provided and can create a dynamic to the provision level across space. We compare treatments in which the endowments are homogeneous with those where some subjects receive a large, some a low endowment. For this heterogeneous case, we specifically study how the spatial distribution of different income levels affects the investment decisions. We investigate and compare two different heterogeneity allocations, namely one in which all three rich players are pooled vs. where they are alternating, i.e. they all are located between low income participants.
External Link(s)

Registration Citation

Koch, Juliane, Andreas Lange and Lorenzo Romero. 2023. "Public Good Provision in Overlapping Neighborhoods." AEA RCT Registry. January 23.
Experimental Details


Intervention Start Date
Intervention End Date

Primary Outcomes

Primary Outcomes (end points)
• Payoff levels (aggregate and by endowment types)
• Investments (level and location, aggregate and by endowment type)

Specific Hypotheses:
• Closed neighborhoods (T1, T2) to resemble existing literature findings on linear public goods (investments positive, but declining in both T1, T2); high endowment types investing larger absolute, but smaller relative share of endowment than low endowment players; potentially detrimental total effect of heterogeneity (T2 vs. T1) on investments and payoff.
• Overlapping neighborhoods (T3, T4, T5): players payoff depends not only on level, but location of investments, players predicted to invest only in their own neighborhood.
• T3 vs. T1: larger provision levels over time than in T1 as change of location of own investments allows for implicit punishment of underperforming neighbor.
• T5,T4 vs. T2: the spatial setting allows to address equity concerns by changing the location an not necessarily the level of total investments. Thus: rich players expected to contribute more in T4 /T5 than in T2 resulting in larger total payoffs, potentially also less inequality In T4, but larger inequality in T5 where rich players are clustered. Players predicted to invest in location(s) of own neighborhood which neighbors the largest number of neighboring high endowment players.
• Adjustment of investments over time: size and location of investments respond to type and investment decisions of neighbor as well as neighbor’s neighbor. Thus, we expect an investment into the neighborhoods of rich players as more likely, with dynamic changes of investment locations in response to relative “kindness” of left vs. right neighbors.
• We explore if individual investments are governed by efficiency/equity/equality concerns. Whereas equity and equality can be reached simultaneously in the homogeneous settings (T1 and T3), these are competing hypotheses in the heterogeneous settings (T2, T4 and T5).

Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
• Dynamic adjustments of investment decisions
Also, further control variables from a short questionnaire at the end of the experiment:
• Demographics: Sex, age, field of study.
• Social preferences: risk-taking, prosociality.
• Task-related opinions: satisfaction with their own/their groupmembers’ decisions, perceived generosity of their own/their groupmembers’ decisions.
• Task comprehension
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
We introduce a spatial public good game with overlapping neighborhoods. Six subjects interact and are located on a circle. Subjects are identified with their respective location (i ∈ {A,B,C,D,E,F}. For each subject, we define a neighborhood Ni. We consider closed and overlapping neighborhoods. The former serves as a baseline and forms a variant of a typical three-player public good game, NA = NB = NC = {A,B,C} and ND = NE = NF = {D,E, F}. In the latter, the neighborhood of a player is given by her location and her two direct neighbors. That is, NA = {A,B, F}, NB = {A,B,C}, NC = {B,C,D}, . . . ,NF = {E, F,A}. Here, each experimental subject benefits from public good investments in his location as well as the location of her two neighbors. Conversely, investments in any location benefit three players.
The experimental treatments vary the endowments the individual players receive. We compare homogenous endowments of all players with heterogenous settings where three players receive a large, and three players receive a small endowment. In such heterogenous settings, the spatial distribution matters: we compare clustered endowment settings where three direct neighbors are rich (A, B,C) while the other three are poos (D,E,F) and alternating endowment settings where rich and poor alternate (A, C, E rich; B,D,F poor). We consider this experimental setting as both novel and empirically relevant.
Our experiment consists of five treatments. As a baseline, we reconsider “closed” neighborhood settings where groups of three individuals play a standard public good game. The only variant to the typical public good setup is the simultaneous presence (and information about) another group as well as the option to invest in any location. In this closed neighborhood setting, we consider a homogeneous income treatment, CNhom, where all agents have an endowment of 30, and a heterogenous setting, CNhet, where in one group two players are endowed with 20 and one player with 40, while in the other group two players are endowed with 40 and one player with 20 tokens. The other treatments introduce the overlapping neighborhoods as described above. Again, we consider homogenous endowments, ONhom, and two heterogenous settings, ONhetCL and ONhetAL, in which endowments are clustered or alternate, respectively. We otherwise stick to a linear public good setting. That is, each player can invest in any location. The payoff from investments to a player then is proportional to the total sum of investments in her neighborhood.
The experimental sessions are scheduled to begin by the end of January 2023. They will be conducted by the experimental laboratory of the University of Hamburg (Germany) in an in-person lab setting. The University of Hamburg does not issue IRBs but instead they have an internal ethical approval process in which a 'Declaration of compliance with Terms of Use and Ethical Standards' (DoC) needs to be signed and submitted. The details of the experiment are hereby approved by the dean of the faculty. The DoC has been submitted and approval has been received.
Experimental Design Details
Not available
Randomization Method
Randomization done in office by a computer.
Randomization Unit
Randomization into treatments as well as locations and roles within the experiment done by computer.
Was the treatment clustered?

Experiment Characteristics

Sample size: planned number of clusters
Sample size: planned number of observations
The experiment will involve 90 participants, i.e. 15 independent observations of groups of six, per treatment. Given the five treatments, we plan to recruit a total of 450 participants.
Sample size (or number of clusters) by treatment arms
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