Bureaucratic discretion and executive delegation are central topics in political economy and political science. The previous empirical literature has measured discretion and delegation by manually coding large bodies of legislation. Drawing from computational linguistics, we provide an automated procedure for
measuring discretion and delegation in legal texts to facilitate large-scale empirical analysis. The method uses information in syntactic parse trees to identify legally relevant provisions, as well as agents and delegated actions. We undertake two applications. First, we produce a measure of bureaucratic discretion by looking at the level of legislative detail for US states and find that this measure increases after reforms giving agencies more independence. This eect is consistent with an agency cost model, where a more independent bureaucracy requires more specific instructions (less discretion) to avoid bureaucratic drift. Second, we construct measures of delegation to governors in state legislation. Consistent with previous estimates using non-text metrics, we find that executive delegation increases under unified government