2022
DOI: 10.1016/j.socnet.2022.02.007
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A network-based explanation of inequality perceptions

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Cited by 9 publications
(7 citation statements)
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“…We are thus able to replicate the main finding of the canonical model of 'expenditure cascades' by Frank et al (2014) within plausible and endogenously evolving social networks. However, we also find that effect sizes are both state-dependent for the very same behavioural parameters and decrease with homophily because for the given graph-generating process, two counteracting effects mediate the effect of actual inequality on perceived inequality (Schulz et al, 2022). Firstly, holding the network-topology constant, increases in aggregate inequality also increase inequality within perception groups and thus decrease savings.…”
Section: Inequality and Aggregate Savingsmentioning
confidence: 58%
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“…We are thus able to replicate the main finding of the canonical model of 'expenditure cascades' by Frank et al (2014) within plausible and endogenously evolving social networks. However, we also find that effect sizes are both state-dependent for the very same behavioural parameters and decrease with homophily because for the given graph-generating process, two counteracting effects mediate the effect of actual inequality on perceived inequality (Schulz et al, 2022). Firstly, holding the network-topology constant, increases in aggregate inequality also increase inequality within perception groups and thus decrease savings.…”
Section: Inequality and Aggregate Savingsmentioning
confidence: 58%
“…The link function's exponential character ensures that those with large income differences become unlikely picks even at low homophily strengths. For an extensive analytical discussion of this linkage behaviour, see Schulz et al (2022). While our linkage function represents a tractable reduced-form representation of the empirically wellestablished homophily, Falk and Knell (2004) demonstrate that endogenous reference groups can emerge from the social comparisons of agents aiming to self-improve and self-enhance.…”
Section: Network Generationmentioning
confidence: 93%
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