Much of our understanding of corporations builds on the idea that managers, when they are not closely monitored, will pursue goals that are not in shareholders' interests. But what goals would managers pursue? This paper uses variation in corporate governance generated by state adoption of antitakeover laws to empirically map out managerial preferences. We use plant-level data and exploit a unique feature of corporate law that allows us to deal with possible biases associated with the timing of the laws. We find that when managers are insulated from takeovers, worker wages (especially those of white-collar workers) rise. The destruction of old plants falls, but the creation of new plants also falls. Finally, overall productivity and profitability decline in response to these laws. Our results suggest that active empire building may not be the norm and that managers may instead prefer to enjoy the quiet life.

Self-control problems change the logic of agency theory by partly aligning the interests of the firm and worker: both now value contracts that elicit future effort. Findings from a year-long field experiment with full-time data entry workers support this idea. First, workers increase output by voluntarily choosing dominated contracts (which penalize low output but give no additional rewards for high output). Second, effort increases closer to (randomly assigned) paydays. Third, the contract and payday effects are strongly correlated within workers, and this correlation grows with experience. We suggest that workplace features such as high-powered incentives or effort monitoring may provide self-control benefits.

Economic models are evaluated by testing the correctness of their predictions. We suggest an additional measure, “completeness”: the fraction of the predictable variation in the data that the model captures. We calculate the completeness of prominent models in three problems from experimental economics: assigning certainty equivalents to lotteries, predicting initial play in games, and predicting human generation of random sequences. The completeness measure reveals new insights about these models, including how much room there is for improving their predictions.