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

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  • Daniel Boller
  • Michael Lechner
  • Gabriel Okasa

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.

Suggested Citation

  • Daniel Boller & Michael Lechner & Gabriel Okasa, 2021. "The Effect of Sport in Online Dating: Evidence from Causal Machine Learning," Papers 2104.04601, arXiv.org.
  • Handle: RePEc:arx:papers:2104.04601
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    1. Stefan Wager & Susan Athey, 2018. "Estimation and Inference of Heterogeneous Treatment Effects using Random Forests," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 113(523), pages 1228-1242, July.
    2. Felfe, Christina & Lechner, Michael & Steinmayr, Andreas, 2011. "Sport and Child Development," Economics Working Paper Series 1135, University of St. Gallen, School of Economics and Political Science.
    3. Michael C Knaus & Michael Lechner & Anthony Strittmatter, 2021. "Machine learning estimation of heterogeneous causal effects: Empirical Monte Carlo evidence," The Econometrics Journal, Royal Economic Society, vol. 24(1), pages 134-161.
    4. Lechner, Michael, 2018. "Modified Causal Forests for Estimating Heterogeneous Causal Effects," IZA Discussion Papers 12040, Institute of Labor Economics (IZA).
    5. Soohyung Lee, 2016. "Effect of Online Dating on Assortative Mating: Evidence from South Korea," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 31(6), pages 1120-1139, September.
    6. Cockx, Bart & Lechner, Michael & Bollens, Joost, 2023. "Priority to unemployed immigrants? A causal machine learning evaluation of training in Belgium," Labour Economics, Elsevier, vol. 80(C).
    7. Fricke, Hans & Lechner, Michael & Steinmayr, Andreas, 2018. "The effects of incentives to exercise on student performance in college," Economics of Education Review, Elsevier, vol. 66(C), pages 14-39.
    8. Michael Lechner & Anthony Strittmatter, 2019. "Practical procedures to deal with common support problems in matching estimation," Econometric Reviews, Taylor & Francis Journals, vol. 38(2), pages 193-207, February.
    9. Brad R. Humphreys & Logan McLeod & Jane E. Ruseski, 2014. "Physical Activity And Health Outcomes: Evidence From Canada," Health Economics, John Wiley & Sons, Ltd., vol. 23(1), pages 33-54, January.
    10. Guido W. Imbens & Jeffrey M. Wooldridge, 2009. "Recent Developments in the Econometrics of Program Evaluation," Journal of Economic Literature, American Economic Association, vol. 47(1), pages 5-86, March.
    11. Pfeifer, Christian & Cornelißen, Thomas, 2010. "The impact of participation in sports on educational attainment--New evidence from Germany," Economics of Education Review, Elsevier, vol. 29(1), pages 94-103, February.
    12. Caruso, Raul, 2011. "Crime and sport participation: Evidence from Italian regions over the period 1997–2003," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 40(5), pages 455-463.
    13. Hodler, Roland & Lechner, Michael & Raschky, Paul A., 2020. "Reassessing the Resource Curse using Causal Machine Learning," Economics Working Paper Series 2016, University of St. Gallen, School of Economics and Political Science.
    14. Lechner, Michael, 2009. "Long-run labour market and health effects of individual sports activities," Journal of Health Economics, Elsevier, vol. 28(4), pages 839-854, July.
    15. Gérard Biau & Erwan Scornet, 2016. "Rejoinder on: A random forest guided tour," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 25(2), pages 264-268, June.
    16. Susan Athey & Guido W. Imbens, 2019. "Machine Learning Methods That Economists Should Know About," Annual Review of Economics, Annual Reviews, vol. 11(1), pages 685-725, August.
    17. Huang, Haifang & Humphreys, Brad R., 2012. "Sports participation and happiness: Evidence from US microdata," Journal of Economic Psychology, Elsevier, vol. 33(4), pages 776-793.
    18. Athey, Susan & Imbens, Guido W., 2019. "Machine Learning Methods Economists Should Know About," Research Papers 3776, Stanford University, Graduate School of Business.
    19. Gunter J. Hitsch & Ali Hortaçsu & Dan Ariely, 2010. "Matching and Sorting in Online Dating," American Economic Review, American Economic Association, vol. 100(1), pages 130-163, March.
    20. Gérard Biau & Erwan Scornet, 2016. "A random forest guided tour," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 25(2), pages 197-227, June.
    21. Rooth, Dan-Olof, 2011. "Work out or out of work -- The labor market return to physical fitness and leisure sports activities," Labour Economics, Elsevier, vol. 18(3), pages 399-409, June.
    22. Michael J. Rosenfeld & Reuben J. Thomas & Sonia Hausen, 2019. "Disintermediating your friends: How online dating in the United States displaces other ways of meeting," Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, vol. 116(36), pages 17753-17758, September.
    23. David Richter & Jürgen Schupp, 2015. "The SOEP Innovation Sample (SOEP IS)," Schmollers Jahrbuch : Journal of Applied Social Science Studies / Zeitschrift für Wirtschafts- und Sozialwissenschaften, Duncker & Humblot, Berlin, vol. 135(3), pages 389-400.
    24. Susan Athey, 2018. "The Impact of Machine Learning on Economics," NBER Chapters, in: The Economics of Artificial Intelligence: An Agenda, pages 507-547, National Bureau of Economic Research, Inc.
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    More about this item

    JEL classification:

    • J12 - Labor and Demographic Economics - - Demographic Economics - - - Marriage; Marital Dissolution; Family Structure
    • Z29 - Other Special Topics - - Sports Economics - - - Other
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics

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