Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/282662 
Year of Publication: 
2023
Series/Report no.: 
IZA Discussion Papers No. 16535
Publisher: 
Institute of Labor Economics (IZA), Bonn
Abstract: 
Parsimony is a desirable feature of economic models but almost all human behaviors are characterized by vast individual variation that appears to defy parsimony. How much parsimony do we need to give up to capture the fundamental aspects of a population's distributional preferences and to maintain high predictive ability? Using a Bayesian nonparametric clustering method that makes the trade-off between parsimony and descriptive accuracy explicit, we show that three preference types - an inequality averse, an altruistic and a predominantly selfish type - capture the essence of behavioral heterogeneity. These types independently emerge in four different data sets and are strikingly stable over time. They predict out-of-sample behavior equally well as a model that permits all individuals to differ and substantially better than a representative agent model and a state-of-the-art machine learning algorithm. Thus, a parsimonious model with three stable types captures key characteristics of distributional preferences and has excellent predictive power.
Subjects: 
distributional preferences
altruism
inequality aversion
preference heterogeneity
stability
out-of-sample prediction
parsimony
Bayesian nonparametrics
JEL: 
D31
D63
C49
C90
Document Type: 
Working Paper

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