Measuring quality effects in equilibrium

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.