This paper aims to measure differences in risk behavior among expert chess players. The study employs a panel data set on international chess with 1.4 million games recorded over a period of 11 years. The structure of the data set allows us to use individual fixed-effect estimations to control for aspects such as innate ability as well as other characteristics of the players. Most notably, the data contains an objective measure of individual playing strength, the so-called Elo rating. In line with previous research, we find that women are more risk-averse than men. A novel finding is that men choose more aggressive strategies when playing against female opponents even though such strategies reduce their winning probability.

This paper presents empirical findings on gender differences in time preference and time inconsistency which are based on international chess data from 1.5 million expert games. Controls are included for age, nationality and playing strength where the latter accounts for gender differences in productivity. Impatience is measured by considering preferences for different game durations. Inconsistency is measured by exploiting the 40^th move time control, where over-consumption of thinking time is inefficient. The results reveal that men are more impatient while women are more time inconsistent. Moreover, the difference in impatience increases with expertise while the difference in inconsistency decreases.

In this study we explore the relationship between attractiveness and risk taking in chess. We examine whether people use riskier strategies when playing with attractive opponents, whether this affects performance, and whether there are gender differences in the reaction to an attractive opponent. We use a large international panel dataset on chess competitions which includes a control for the players’ skill in chess. We combine this data with results from a large survey on an online labor market where participants were asked to rate the photos of 626 expert chess players. Our results suggest that male chess players choose significantly riskier strategies when playing against an attractive female opponent, although this does not improve their performance. Women’s strategies are not affected by the attractiveness of the opponent.

This paper examines if expert chess players in repeated chess games with the same opponent learn about the opponent’s type and adapt their choice of future strategies accordingly. Additionally, it shows how matching background characteristics such as gender, age, nationality and risk preferences affect the choice of strategy. The study employs a large international panel dataset with controls for age, gender, nationality, risk preferences and playing strength whereby the latter accounts for productivity. The findings show that chess players do indeed learn about the opponent’s type and that more recent outcomes have a greater influence than earlier outcomes. Moreover, players with similar background characteristics coordinate better than players of different gender or nationality but this difference decreases as the players learn about their opponent’s type. This suggests that screening discrimination decreases when players learn about the type of the opponent.