It is never too LATE: a new look at local average treatment effects with or without defiers

B-Tier
Journal: The Econometrics Journal
Year: 2023
Volume: 26
Issue: 3
Pages: 378-404

Score contribution per author:

0.670 = (α=2.01 / 3 authors) × 1.0x B-tier

α: calibrated so average coauthorship-adjusted count equals average raw count

Abstract

SummaryIn heterogeneous treatment effect models with endogeneity, identification of the local average treatment effect (LATE) typically relies on the availability of an exogenous instrument monotonically related to treatment participation. First, we demonstrate that a strictly weaker local monotonicity condition—invoked for specific potential outcome values rather than globally—identifies the LATEs on compliers and defiers. Second, we show that our identification results apply to subsets of compliers and defiers when imposing an even weaker local compliers-defiers assumption that allows for both types at any potential outcome value. We propose estimators that are potentially more efficient than two-stage least squares (2SLS) in finite samples, even in cases where 2SLS is consistent. Finally, we provide an empirical application to estimating returns to education using the quarter of birth instrument.

Technical Details

RePEc Handle
repec:oup:emjrnl:v:26:y:2023:i:3:p:378-404.
Journal Field
Econometrics
Author Count
3
Added to Database
2026-01-26