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.
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.