Economic Decision-Making in Groups: An Experimental Analysis of the Effect of Group Size and Gender Composition.

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Many decisions in firms, organizations, and everyday life are not taken by individuals, but instead jointly by the members of a group. Examples include solving complex problems, devising strategies, compliance behavior in firms and organizations, and bargaining with other parties. This project is concerned with two main research questions regarding such informal group decision-making:1.) How does group size systematically affect the decision-making of groups? The vast majority of existing experimental studies on group decisions has focused on dyads or triads (i.e. groups of two and three members respectively) and the comparison with individual decision-making. Very little is known about the systematic effects when the group size is increased from two to three members or to even larger groups. This is, however, an empirically relevant case, since teams in firms and organizations are often larger than dyads or triads. Moreover, countervailing effects are expected from increasing the group size, such that it remains unclear what an optimal group size might be.2.) How are group decisions affected by the group gender composition? There exists an extensive experimental literature on how gender differences in preferences affect individual decision-making. However, there is no systematic evidence, whether such gender differences at the individual level are amplified or attenuated in group settings. By analyzing the effects of the gender composition in relatively large groups, we can shed light on this issue and contribute to the overall agenda towards a better understanding of gender differences in behavior.By running five laboratory experiments, each including informal group decision-making with groups of different size, we want to take a first step towards narrowing the literature gaps mentioned above. We thereby want to focus on three different areas of decision-making (intellective, judgmental and strategic) to provide evidence across different task types.