This paper analyzes the conditions under which more legislation contributes to economic growth. In the context of US states, we apply natural language processing tools to measure legislative flows for the years 1965–2012. We implement a novel shift-share design for text data, where the instrument for legislation is leave-one-out legal topic flows interacted with pretreatment legal topic shares. We find that at the margin, higher legislative output causes more economic growth. Consistent with more complete laws reducing ex post holdup, we find that the effect is driven by the use of contingent clauses, is largest in sectors with high relationship-specific investments, and is increasing with local economic uncertainty.

We study judicial in-group bias in Indian criminal courts using newly collected data on over 5 million criminal case records from 2010–2018. After classifying gender and religious identity with a neural network, we exploit quasi-random assignment of cases to judges to determine whether judges favor defendants with similar identities to themselves. In the aggregate, we estimate tight zero effects of in-group bias based on shared gender or religion, including in settings where identity may be especially salient, such as when the victim and defendant have discordant identities. Proxying caste similarity with shared last names, we find a degree of in-group bias, but only among people with rare names; its aggregate impact remains small.