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Good Samaritans in Networks: An Experiment on How Networks Influence Egalitarian Sharing and the Evolution of Inequality.

Chiang YS - PLoS ONE (2015)

Bottom Line: How inequality evolves as a result of egalitarian sharing is determined by the structure of "who gives whom".Inequality is significantly reduced in networks where the very rich and the very poor are connected so that income discrepancy is salient enough to motivate the rich to share their incomes with the poor.The study suggests that social networks make a difference in how egalitarian sharing influences the evolution of inequality.

View Article: PubMed Central - PubMed

Affiliation: Department of Sociology, The Chinese University of Hong Kong, Hong Kong, China.

ABSTRACT
The fact that the more resourceful people are sharing with the poor to mitigate inequality-egalitarian sharing-is well documented in the behavioral science research. How inequality evolves as a result of egalitarian sharing is determined by the structure of "who gives whom". While most prior experimental research investigates allocation of resources in dyads and groups, the paper extends the research of egalitarian sharing to networks for a more generalized structure of social interaction. An agent-based model is proposed to predict how actors, linked in networks, share their incomes with neighbors. A laboratory experiment with human subjects further shows that income distributions evolve to different states in different network topologies. Inequality is significantly reduced in networks where the very rich and the very poor are connected so that income discrepancy is salient enough to motivate the rich to share their incomes with the poor. The study suggests that social networks make a difference in how egalitarian sharing influences the evolution of inequality.

No MeSH data available.


Inequalities of the end-round distributions measured by the Gini coefficient for each network treatment.The segments represent the 95% confidence interval. The vertical dotted line shows the inequality level of the original distribution.
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pone.0128777.g002: Inequalities of the end-round distributions measured by the Gini coefficient for each network treatment.The segments represent the 95% confidence interval. The vertical dotted line shows the inequality level of the original distribution.

Mentions: Our primary objective is to compare income distributions in the initial and the final round of the experiment to see whether inequality improves or not. Fig 2 presents the distribution of inequality levels measured by the Gini coefficient for each network treatment. We calculate the Gini coefficient of the end-round distribution for each session. Using session as the unit of analysis, we compare the initial and the end-round Gini coefficients by running the Wilcoxon Signed Rank test (for more details, please see S4 File). The test shows that the Gini coefficient of the end-round distribution is lower than the original income distribution in the Lattice_Hetero and the SF_Negative network treatment (W = 0, p = 0.01 and W = 0, p = 0.03), but not in the other three network treatments (W = 5; p = 0.31 for Full; W = 15; p = 0.44 for Lattice_Homo and W = 14; p = 0.56 for SF_Positive).


Good Samaritans in Networks: An Experiment on How Networks Influence Egalitarian Sharing and the Evolution of Inequality.

Chiang YS - PLoS ONE (2015)

Inequalities of the end-round distributions measured by the Gini coefficient for each network treatment.The segments represent the 95% confidence interval. The vertical dotted line shows the inequality level of the original distribution.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4465669&req=5

pone.0128777.g002: Inequalities of the end-round distributions measured by the Gini coefficient for each network treatment.The segments represent the 95% confidence interval. The vertical dotted line shows the inequality level of the original distribution.
Mentions: Our primary objective is to compare income distributions in the initial and the final round of the experiment to see whether inequality improves or not. Fig 2 presents the distribution of inequality levels measured by the Gini coefficient for each network treatment. We calculate the Gini coefficient of the end-round distribution for each session. Using session as the unit of analysis, we compare the initial and the end-round Gini coefficients by running the Wilcoxon Signed Rank test (for more details, please see S4 File). The test shows that the Gini coefficient of the end-round distribution is lower than the original income distribution in the Lattice_Hetero and the SF_Negative network treatment (W = 0, p = 0.01 and W = 0, p = 0.03), but not in the other three network treatments (W = 5; p = 0.31 for Full; W = 15; p = 0.44 for Lattice_Homo and W = 14; p = 0.56 for SF_Positive).

Bottom Line: How inequality evolves as a result of egalitarian sharing is determined by the structure of "who gives whom".Inequality is significantly reduced in networks where the very rich and the very poor are connected so that income discrepancy is salient enough to motivate the rich to share their incomes with the poor.The study suggests that social networks make a difference in how egalitarian sharing influences the evolution of inequality.

View Article: PubMed Central - PubMed

Affiliation: Department of Sociology, The Chinese University of Hong Kong, Hong Kong, China.

ABSTRACT
The fact that the more resourceful people are sharing with the poor to mitigate inequality-egalitarian sharing-is well documented in the behavioral science research. How inequality evolves as a result of egalitarian sharing is determined by the structure of "who gives whom". While most prior experimental research investigates allocation of resources in dyads and groups, the paper extends the research of egalitarian sharing to networks for a more generalized structure of social interaction. An agent-based model is proposed to predict how actors, linked in networks, share their incomes with neighbors. A laboratory experiment with human subjects further shows that income distributions evolve to different states in different network topologies. Inequality is significantly reduced in networks where the very rich and the very poor are connected so that income discrepancy is salient enough to motivate the rich to share their incomes with the poor. The study suggests that social networks make a difference in how egalitarian sharing influences the evolution of inequality.

No MeSH data available.