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Measuring quality effects in equilibrium.

Seth Richards-Shubik1, Mark S Roberts2, Julie M Donohue2

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This summary is machine-generated.

Healthcare provider demand models often lack price data, potentially underestimating consumer responses to quality. This study introduces a congestion effect to improve quality effect forecasts, particularly in heart surgery markets.

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Area of Science:

  • Health economics
  • Healthcare market analysis
  • Econometrics

Background:

  • Standard healthcare demand models often exclude market-clearing variables like price or waiting times.
  • This omission can lead to underestimated consumer responsiveness to healthcare quality.
  • Limited provider capacity can prevent consumers from accessing higher quality care, creating bias.

Purpose of the Study:

  • To develop a tractable method for estimating consumer response to quality in healthcare markets.
  • To address the attenuation bias caused by omitted market-clearing factors in demand models.
  • To improve the accuracy of forecasts for consumer behavior regarding healthcare quality.

Main Methods:

  • Incorporation of a congestion effect into standard discrete-choice models.
  • Analytical demonstration of the method's ability to improve quality effect forecasts.
  • Empirical application to the heart surgery market.

Main Results:

  • The proposed method analytically improves forecasts of consumer response to quality.
  • Empirical analysis in the heart surgery market revealed significant attenuation bias in estimated quality effects.
  • The congestion effect is crucial for accurately modeling consumer choice in capacity-constrained healthcare markets.

Conclusions:

  • Adding a congestion effect to discrete-choice models provides a more accurate estimation of quality effects in healthcare.
  • Failure to account for provider capacity and congestion leads to underestimation of quality's impact on consumer demand.
  • This methodology enhances the reliability of healthcare market analysis and policy implications.