Legal aid eligibility and court outcomes: a design-based double-machine-learning approach

Abstract

Equality before the law is a human right, and access to high-quality legal aid for indigent defendants is essential to enforce it. In a context where all defendants have access to a lawyer, I study the impact of denying legal aid on court outcomes. I combine double machine learning and a new administrative dataset linking aid to court outcomes in New South Wales, Australia, to learn the assignment function, whose inputs are known. I find that applicants who fail the means test and hire private lawyers are 10 percentage points less likely to be incarcerated than if they passed and relied on legal aid. Given an average incarceration length of nearly four years, this gap is significant. However, I find evidence suggesting that they spend more time in jail if they are incarcerated. A government preference for broad access to aid over allocated time per case could explain this pattern. Keywords: Indigent Defense, Crime, Criminal Justice. JEL: I30, K14, H44.