The Effect of Sport in Online Dating: Evidence from Causal Machine Learning

Daniel Boller (), Michael Lechner and Gabriel Okasa ()
Additional contact information
Daniel Boller: University of St. Gallen
Gabriel Okasa: University of St. Gallen

No 14259, IZA Discussion Papers from Institute of Labor Economics (IZA)

Abstract: Online dating emerged as a key platform for human mating. Previous research focused on socio-demographic characteristics to explain human mating in online dating environments, neglecting the commonly recognized relevance of sport. This research investigates the effect of sport activity on human mating by exploiting a unique data set from an online dating platform. Thereby, we leverage recent advances in the causal machine learning literature to estimate the causal effect of sport frequency on the contact chances. We find that for male users, doing sport on a weekly basis increases the probability to receive a first message from a woman by 50%, relatively to not doing sport at all. For female users, we do not find evidence for such an effect. In addition, for male users the effect increases with higher income.

Keywords: online dating; sports economics; big data; causal machine learning; effect heterogeneity; Modified Causal Forest (search for similar items in EconPapers)
JEL-codes: C21 C45 J12 Z29 (search for similar items in EconPapers)
Pages: 100 pages
Date: 2021-04
New Economics Papers: this item is included in nep-big, nep-pay and nep-spo
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Related works:
Working Paper: The Effect of Sport in Online Dating: Evidence from Causal Machine Learning (2021)
Working Paper: The Effect of Sport in Online Dating: Evidence from Causal Machine Learning (2021)
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