Firms are starting to replace humans with algorithms in important screening decisions, but there are potential spillovers of human biases contained in datasets to subsequent algorithmic predictions. When these biases are motivated by human prejudices, there are risks of algorithms perpetuating discrimination. I prove that when datasets are generated by a sufficiently discriminatory human, firms are more profitable when training discriminatory algorithms. If instead enough affirmative action is instituted in favor of a disadvantaged group, firms are more profitable when training algorithms that inflate scores for this group, but this effect diminishes with excess affirmative action.

The use of equity incentives is significantly greater in countries with stronger insider trading restrictions, and these higher incentives are associated with higher total pay. These findings are robust to alternative definitions of insider trading restrictions and enforcement, and to panel regressions with country fixed effects. We also find significant increases in top executive pay and the use of equity-based incentives in the period immediately following the initial enforcement of insider trading laws. We conclude that insider trading laws are one channel through which cross-country differences in pay practices can be explained.