Altruism in Networks: A Field Experiment on Social Closeness, Preferences and Transfers

Last registered on November 05, 2021

Pre-Trial

Trial Information

General Information

Title
Altruism in Networks: A Field Experiment on Social Closeness, Preferences and Transfers
RCT ID
AEARCTR-0008485
Initial registration date
November 03, 2021
Last updated
November 05, 2021, 7:59 PM EDT

Locations

Primary Investigator

Affiliation

Other Primary Investigator(s)

PI Affiliation
University of Nottingham
PI Affiliation
University of Nottingham
PI Affiliation
University of Nottingham

Additional Trial Information

Status
In development
Start date
2021-11-08
End date
2021-12-01
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
Understanding the mechanisms that explain altruistic actions has been subject to research for decades. To extend our knowledge of altruism in more complex situations, we investigate the role of social cohesion on altruistic transfers in network settings. To this end, we elicit a social network of friends and subsequently measure the relationship levels between them. Then, conditional on the elicited social network and relationships, we measure a series of mechanisms as potential mediational factors between social cohesion and transfer decisions with more than two players involved. Our set-up allows us to identify the degree to which individuals embedded in social networks take into account subsequent transfers between other parties, network information and individual identity.
External Link(s)

Registration Citation

Citation
Baader, Malte et al. 2021. "Altruism in Networks: A Field Experiment on Social Closeness, Preferences and Transfers." AEA RCT Registry. November 05. https://doi.org/10.1257/rct.8485-1.0
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Experimental Details

Interventions

Intervention(s)
Intervention Start Date
2021-11-08
Intervention End Date
2021-12-01

Primary Outcomes

Primary Outcomes (end points)
Transfers in networks in the presence of different levels of altruism and cohesion
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
In this study, we investigate the role of social cohesion on altruistic transfers in networks. To this end, we first elicit a social network of friends and measure the relationship levels between them. Then, for a variety of indicators of social cohesion, we measure social norms and other-regarding preferences as potential mediational factors. Ultimately, we want to experimentally explore altruistic transfers in networks. We thereby draw on the theoretical framework by Bourlès et al. (2017) by investigating individual transfers with more than two players involved. For that, we isolate 3-player networks from the overall social network, where one player (Player A) can transfer to two other players (Player B & Player C) and Player B can also transfer to Player C (see overview below).

Player A ---------> Player B; Player A ---------> Player C; Player B ---------> Player C

Thus, any transfer by Player A should take into account a potential additional transfer between the other two players. Moreover, Player B also needs to take into account the potential transfer between Player A and C when making their transfer decisions.
Our set-up allows us to identify the extent to which individuals embedded in social networks take into account subsequent transfers between other parties, network information and players' identity. Moreover, we are able to provide a comprehensive understanding and analysis of the relationship between social closeness, distributional preferences and their explanatory value to examine transfers in network settings.
Experimental Design Details
Our study is divided into 2 different parts. In the first part, we elicit a friend network of students and measure the relationship levels between them. To this end, we invite second- and third-year undergraduate students from the University of Nottingham to participate in our study. The students are asked to name 10 other students in their degree and year and indicate their closeness to each other person they list using a computerised extended version of the ‘Inclusion of the Other in Self’ Scale (IOS, Aron et al., 1992; Gächter et al., 2015). Moreover, following the design by Leider et al. (2009), we further ask how much time they spend with each of the 10 listed students. As an incentive, all subjects have a 50% chance to win a bonus if the student they name also lists them in return. At the end of this part, we further ask all participants to indicate social appropriateness levels for varying degrees of relationships using the method developed by Krupka & Weber (2013) while also eliciting a small set of background characteristics (e.g., gender, age, etc.) as well as survey measures of pro-sociality (Falk et al. 2018).
The second part of the experiment consist of a series of allocation decisions that we collect across two distinct waves. Before moving to the more complex network decisions, all subjects face a series of two player allocation decisions. Across the waves, we elicit six distinct distributional preference parameters for each subject using a modified version of Fisman et al (2007). Following Leider et al. (2009), we elicit parameters for different social distances (SD) in the network structure, namely a direct friend (SD = 1) and a friend of a friend (SD = 2). Moreover, we also elicit baseline altruism towards an unknown student. As future interactions are considered to play a fundamental role in altruistic actions, we also measure preference parameters for all social distances with anonymous – as well as non-anonymous – earnings. Lastly, to identify the extent to which revealed distributional preferences predict altruistic giving, we also ask all subjects to make three additional dictator game decisions for each of the six scenarios described above.
To answer our main research question, we then ask subjects to make 4 transfer decisions in a slightly more complex network setting. For that, we designed a 3-player network, where one player (Player A) can transfer to two other players (Player B & Player C) and Player B can also transfer to Player C (see overview below).

Player A ---------> Player B; Player A ---------> Player C; Player B ---------> Player C

Thus, any transfer by Player A should take into account a potential additional transfer between the other two players. Moreover, Player B also needs to take into account the potential transfer between Player A and C when making their transfer decisions. To utilise this, we ask participants to make decisions as player B in wave 2 and as player A in wave 1. As Player B, we ask them to state their conditional transfers to C, i.e. for each possible amount that could be transferred to them by player A, they need to state how much they would pass on to player C.
In their recent publication, Bourlès et al. (2017) investigate the importance of conditional transfers in a network setting. Our triangular structure provides a simple version of a network that is nevertheless sufficient to test their theoretical results empirically. Moreover, we also employ a within-subject treatment to test the role of knowing player’s identities on transfer decisions. As a last treatment variation, we explore in how far players infer potential altruistic transfers from knowledge about relationship levels. To this end, we vary the information Player A has about the transfer between Player B and C. Player A either knows only the relationship level (IOS), only the exact conditional transfer, or both. As individuals’ expectations about transfers in the network may in theory play an important role, we also elicit incentivised beliefs about relevant transfers.
This experimental design overall allows us to provide a comprehensive understanding and analysis of the relationship between social closeness, distributional preferences and their explanatory value to examine transfers in network settings.

References

Aron, A., Aron, E. N., & Smollan, D. (1992). Inclusion of other in the self scale and the structure of interpersonal closeness. Journal of personality and social psychology, 63(4), 596.

Bourlès, R., Bramoullé, Y., & Perez‐Richet, E. (2017). Altruism in networks. Econometrica, 85(2), 675-689.

Falk, A., Becker, A., Dohmen, T., Enke, B., Huffman, D., & Sunde, U. (2018). Global evidence on economic preferences. The Quarterly Journal of Economics, 133(4), 1645-1692.

Gächter, S., Starmer, C., & Tufano, F. (2015). Measuring the closeness of relationships: a comprehensive evaluation of the'inclusion of the other in the self'scale. PloS one, 10(6), e0129478.

Krupka, E. L., & Weber, R. A. (2013). Identifying social norms using coordination games: Why does dictator game sharing vary?. Journal of the European Economic Association, 11(3), 495-524.

Leider, S., Möbius, M. M., Rosenblat, T., & Do, Q. A. (2009). Directed altruism and enforced reciprocity in social networks. The Quarterly Journal of Economics, 124(4), 1815-1851.
Randomization Method
For all between-subject treatments we will randomise subjects using a random-number generator. The within-subject anonymous payment treatment is also randomised across waves.
Randomization Unit
Individuals
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
Standard errors will be clustered at the individual level. See below for the expected number of participants.
Sample size: planned number of observations
As sample size depends strongly on the number of students responding to our experimental invitation and thus we cannot perfectly control the number of subjects. We are however aiming for around 600 subjects completing the entire experimental series.
Sample size (or number of clusters) by treatment arms
200 subjects for each of the three information treatment arms (i.e., information about (i) relationship, (ii) transfer, or (iii) both). All other treatments are within subject (i.e., (i) social distance and (ii) cohesion to allocation partner; (ii) information about identity in triangles).
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
IRB

Institutional Review Boards (IRBs)

IRB Name
Nottingham School of Economics Research Ethics Committee
IRB Approval Date
2021-10-13
IRB Approval Number
N/A

Post-Trial

Post Trial Information

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Intervention

Is the intervention completed?
No
Data Collection Complete
Data Publication

Data Publication

Is public data available?
No

Program Files

Program Files
Reports, Papers & Other Materials

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Reports & Other Materials