Comparing 2SLS vs 2SRI for Binary Outcomes and Binary Exposures
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.
Basu acknowledges support from NIH research grants RC4CA155809 and R01CA155329. Coe acknowledges support from National Institute of Nursing Research grant NIH 1R01NR13583 (PI: Van Houtven). Chapman acknowledges support from SMT Inc. (PI: Schooley) and the Institute for Healthcare Improvement (PI: Cozad). We thank two anonymous reviewers for their very useful comments. Opinions expressed are ours and do not reflect those of the University of Washington or the NBER. All errors are our own. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research.