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Oliver Williamson has coined the term "fundamental transformation". It captures the following situation: before they strike a deal, buyer and seller are protected by competition. Yet thereafter they find themselves in a bilateral monopoly. With common knowledge of standard preferences, both sides conclude the contract regardless if its expected value exceeds their outside options. We run an experiment to test whether additional behavioral reasons deter mutually beneficial trade. If the risk materializes, another individual makes a windfall profit. She does so by intentionally exploiting the first individual. The first individual is let down, although she has knowingly exposed herself to this risk. Participants sell the opportunity to enter the contractual relationship at a price below its expected value. This effect is driven by risk aversion, and already present if the risk is stochastic. Behavioral effects are heterogeneous. About a quarter of participants exhibit the hypothesized additional deterrent effect.

The way profits are divided within successful teams imposes different degrees of internal conflict. We experimentally examine how the level of internal conflict, and whether such conflict is transparent to other teams, affects teams' ability to compete vis-a-vis each other, and, consequently, market outcomes. Participants took part in a repeated Bertrand duopoly game between three-player teams which had either the same or different level of internal conflict (uniform vs. mixed). Profit division was either private-pay (high conflict; each member received her own asking price) or equal-pay (low conflict; profits were divided equally). We find that internal conflict leads to (tacit) coordination on high prices in uniform private-pay duopolies, but places private-pay teams at a competitive disadvantage in mixed duopolies. Competition is softened by transparency in uniform markets, but intensified in mixed markets. We propose an explanation of the results and discuss implications for managers and policy makers. (D43, L22, C92).

We provide an example for an errors in variables problem which might be often neglected but which is quite common in lab experimental practice: In one task, attitude towards risk is measured, in another task participants behave in a way that can possibly be explained by their risk attitude. How should we deal with inconsistent behaviour in the risk task? Ignoring these observations entails two biases: An errors in variables bias and a selection bias. We argue that inconsistent observations should be exploited to address the errors in variables problem, which can easily be done within a Bayesian framework.