Leniency Designs: An Operator's Manual

TL;DR

This paper provides a comprehensive guide to designing and analyzing leniency-based econometric studies, introducing the UJIVE estimator for unbiased inference and demonstrating its application through a patent valuation case study.

Contribution

It introduces the UJIVE estimator for unbiased analysis of leniency designs and offers practical tools for assessing assumptions and external validity.

Findings

UJIVE estimator avoids biases in leniency studies

Quasi-random assignment improves validity of treatment effect estimates

Non-clustered standard errors are often appropriate for inference

Abstract

We develop a step-by-step guide to leniency (a.k.a. judge or examiner instrument) designs, drawing on recent econometric literatures. The unbiased jackknife instrumental variables estimator (UJIVE) is purpose-built for leveraging exogenous leniency variation, avoiding subtle biases even in the presence of many decision-makers or controls. We show how UJIVE can also be used to assess key assumptions underlying leniency designs, including quasi-random assignment and average first-stage monotonicity, and to probe the external validity of treatment effect estimates. We further discuss statistical inference, arguing that non-clustered standard errors are often appropriate. A reanalysis of Farre-Mensa et al. (2020), using quasi-random examiner assignment to estimate the value of patents to startups, illustrates our checklist.