| 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 catalogue of oral traditions spanning approximately 1,000 societies. After validating the catalogueâs content by showing that the groupsâ 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 groupâs 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 towards 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 where competitions and challenges are more likely to be harmful than beneficial. Communities with low tolerance towards antisocial behavior, captured by the prevalence of tricksters getting 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. |