| Inhalt: | Abstract
Folklore is the collection of traditional beliefs, customs, and stories of a community passed through the generations by word of mouth. We introduce to economics a unique catalog of oral traditions spanning approximately 1,000 societies. After validating the catalog’s content by showing that the groups’ motifs reflect known geographic and social attributes, we present two sets of applications. First, we illustrate how to fill in the gaps and expand upon a group’s ethnographic record, focusing on political complexity, high gods, and trade. Second, we discuss how machine learning and human classification methods can help shed light on cultural traits, using gender roles, attitudes toward risk, and trust as examples. Societies with tales portraying men as dominant and women as submissive tend to relegate their women to subordinate positions in their communities, both historically and today. More risk-averse and less entrepreneurial people grew up listening to stories wherein competitions and challenges are more likely to be harmful than beneficial. Communities with low tolerance toward antisocial behavior, captured by the prevalence of tricksters being punished, are more trusting and prosperous today. These patterns hold across groups, countries, and second-generation immigrants. Overall, the results highlight the significance of folklore in cultural economics, calling for additional applications. |
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