The Fundamental Properties, Stability and Predictive Power of Distributional Preferences

Ernst Fehr, Thomas Epper and Julien Senn

No 10727, CESifo Working Paper Series from CESifo

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 behaviour 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.

Keywords: distributional preferences; altruism; inequality aversion; preference heterogeneity; stability; out-of-sample prediction; parsimony; Bayesian nonparametrics (search for similar items in EconPapers)
JEL-codes: C49 C90 D31 D63 (search for similar items in EconPapers)
Date: 2023
New Economics Papers: this item is included in nep-cbe, nep-evo, nep-ltv and nep-upt
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Related works:
Working Paper: The Fundamental Properties, Stability and Predictive Power of Distributional Preferences (2023)
Working Paper: The Fundamental Properties, Stability and Predictive Power of Distributional Preferences (2023)
Working Paper: The Fundamental Properties, Stability and Predictive Power of Distributional Preferences (2023)
Working Paper: The fundamental properties, stability and predictive power of distributional preferences (2023)
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