In this project, we intend to complement the recently growing literature (see for instance Card & Dahl, 2011, Quarterly Journal of Economics or Ge, 2018, Journal of Economic Behaviour & Organization) empirically testing Koszegi and Rabin’s (2006, Quarterly Journal of Economics) theoretical prediction, that utility from deviations between (rationally) expected references points and actual outcomes (i.e. gain-loss utility) influences behavior under uncertainty.
In contrast to many other studies, frequently struggling in identifying appropriate reference points when testing for field evidence of reference-dependent behavior, these papers make use of a setting, where (average) reference points can be directly inferred from the betting market, i.e. professional sports. Aim of this project is to contribute to this literature in four ways: First, this is the first project exploring in a real life setting the link between reference points and alcohol use: a ‘phenomenon’, which is deeply rooted in society, widely recognized as a leading risk factor for death and disability and a major contributor to criminal behavior such as family violence.
Second, our particular setting in combination with the available intensive longitudinal multilevel data allows us to further disentangle the cause and consequences of reference point behavior, e.g. by testing the relevance of emotional salience and the degree of emotional ‘involvement’ as a fan of either the home or away team.
Third, studies testing psychological theories of drinking and alcoholism have historically relied on lab experiments in combination with self-reported survey data. While manipulations of emotions are generally difficult to execute and control in the lab, self-reported survey data on alcohol use are subject to recall and/or social desirability biases. As such, our setting might offer for the first time some reliable field evidence for these theories.
Fourth, our specific data structure is expected to require a departure from standard specifications using intensive longitudinal multilevel time series techniques (e.g., Asparouhov, Hamaker, & Muthen, 2018, Structural Equation Modeling). However, such models need to consider sparsity and application/development of new regularization techniques (such as Bayesian adaptive lasso priors in statistical learning). As such, a methodological focus is put on the development and application of Bayesian time series models that blend properties of multilevel models, latent variable models, and generalized additive models.
Research Line: Sports Consumer Behavior
Funding / Support: Funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany’s Excellence Strategy – EXC number 2064/1 – Project number 390727645. For further information, refer to Cluster of Excellence Machine Learning.