AI 中文总结
本研究结合双机器学习方法与澳大利亚新南威尔士州相关行政数据集，探讨拒绝法律援助对法庭结果的影响，发现未通过经济审查并聘请私人律师的申请人被监禁概率更低，但若被监禁则服刑时间更长。
AI 中文摘要
法律面前人人平等是一项人权，为贫困被告提供高质量法律援助是落实这项权利的关键。在所有被告均能获得律师代理的背景下，本研究探讨拒绝法律援助对法庭结果的影响。本研究结合双机器学习（double machine learning）方法与澳大利亚新南威尔士州一项将法律援助与法庭结果关联的新型行政数据集，以学习其输入已知的分配函数。研究发现，未通过经济状况审查并聘请私人律师的申请人，其被监禁的可能性比通过审查并依赖法律援助的申请人低10个百分点；在平均监禁时长近四年的情况下，这一差异具有统计学意义。不过，研究也发现相关证据表明，若此类申请人被监禁，其服刑时间更长，而政府更倾向于扩大法律援助的覆盖范围而非缩短单个案件的处理时间，或可解释这一模式。关键词：贫困辩护、犯罪、刑事司法；JEL分类：I30、K14、H44。
英文摘要
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.
CommentsAccepted for publication at the Journal of Law & Economics
