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Deploying Differential Distance as an Instrumental Variable: Alternative Forms, Estimators, and Specifications.

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Summary

Instrumental variable (IV) methods in health research can yield different causal effect estimates. Differential distance (DD) as an IV shows varied results depending on the approach, impacting healthcare cost analysis.

Keywords:
2SLS2SRIdifferential distanceinpatient psychiatric admissioninstrumental variablelocal instrumental variable

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Area of Science:

  • Health Economics
  • Econometrics
  • Causal Inference

Background:

  • Instrumental variable (IV) methods are crucial for causal inference in health research.
  • The specific type of treatment effect estimated by various IV approaches is often under-examined.
  • Differential distance (DD) is a commonly used IV in health studies, but its application varies.

Purpose of the Study:

  • To investigate how different instrumental variable (IV) approaches, specifically differential distance (DD), affect the estimation of treatment effects in health economics.
  • To evaluate the causal impact of for-profit (FP) versus not-for-profit (NFP) hospital choice on psychiatric inpatient stay costs.
  • To clarify the identification of parameters when using DD as an IV in diverse econometric models.

Main Methods:

  • Literature review summarizing the use of differential distance (DD) as an instrumental variable (IV).
  • Theoretical reasoning and a case study comparing two-stage least squares (2SLS) with binary vs. continuous DD.
  • Application of two-stage residual inclusion (2SRI) with binary and continuous DD, and comparison with local IV approaches.

Main Results:

  • Estimates of treatment effects varied significantly when using two-stage least squares (2SLS) with binary versus continuous differential distance (DD).
  • Two-stage residual inclusion (2SRI) approaches yielded similar treatment effect estimates with both binary and continuous DD, provided adequate control function modeling.
  • 2SRI estimates aligned closely with average treatment effect estimates from local IV approaches, indicating potential for understanding selection heterogeneity.

Conclusions:

  • The choice of instrumental variable (IV) method, particularly the implementation of differential distance (DD), critically influences causal effect estimates in health research.
  • Two-stage residual inclusion (2SRI) offers a more robust approach with differential distance (DD) compared to two-stage least squares (2SLS) for estimating treatment effects.
  • Understanding the heterogeneity of treatment effects is essential for accurately assessing the impact of hospital type (for-profit vs. not-for-profit) on healthcare costs.