Working Paper: NBER ID: w23840
Authors: Anirban Basu; Norma Coe; Cole G. Chapman
Abstract: This study uses Monte Carlo simulations to examine the ability of the two-stage least-squares (2SLS) estimator and two-stage residual inclusion (2SRI) estimators with varying forms of residuals to estimate the local average and population average treatment effect parameters in models with binary outcome, endogenous binary treatment, and single binary instrument. The rarity of the outcome and the treatment are varied across simulation scenarios. Results show that 2SLS generated consistent estimates of the LATE and biased estimates of the ATE across all scenarios. 2SRI approaches, in general, produce biased estimates of both LATE and ATE under all scenarios. 2SRI using generalized residuals minimizes the bias in ATE estimates. Use of 2SLS and 2SRI is illustrated in an empirical application estimating the effects of long-term care insurance on a variety of binary healthcare utilization outcomes among the near-elderly using the Health and Retirement Study.
Keywords: 2SLS; 2SRI; binary outcomes; binary treatments; instrumental variables
JEL Codes: C26; I10; I18
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| 2SLS method (C20) | local average treatment effect (LATE) (C22) |
| treatment (M53) | outcome (P17) |
| 2SRI method (C20) | local average treatment effect (LATE) (C22) |
| 2SRI method (C20) | average treatment effect (ATE) (C22) |
| 2SRI with generalized residuals (C20) | average treatment effect (ATE) (C22) |
| 2SRI with Anscombe residuals (C29) | average treatment effect (ATE) (C22) |