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Area of Science:

  • Econometrics
  • Policy Evaluation
  • Statistical Methods

Background:

  • Regression discontinuity designs are widely used for causal inference.
  • Local linear regression (LLR) is a common estimator in these designs.
  • LLR has limitations when extrapolating treatment effects for binary outcomes.

Purpose of the Study:

  • To propose a novel estimator for regression discontinuity designs with binary outcomes.
  • To overcome the extrapolation limitations of LLR for binary response variables.
  • To provide a method that yields similar estimates to LLR for the original treatment level.

Main Methods:

  • Development of local maximum likelihood (LML) estimators.
  • Comparison of LML with LLR through simulation studies.
  • Application to an empirical case of an income subsidy program's effect on religion.

Main Results:

  • LML estimators effectively handle binary response variables in regression discontinuity.
  • LML estimators avoid the out-of-bounds issue inherent in LLR extrapolation.
  • LML estimators provide comparable results to LLR for the baseline treatment effect.

Conclusions:

  • Local maximum likelihood offers a robust alternative to LLR for binary outcomes in regression discontinuity.
  • The proposed method enhances the reliability of policy effect estimations.
  • Empirical evidence supports the practical utility of LML in program evaluation.