Learning and the value of information: Evidence from health plan report cards
This paper develops a framework to analyze the value of information in the context of health plan choice. We use a Bayesian learning model to estimate the impact and value of information using data from a large employer, which started distributing health plan ratings to its employees in 1997. We estimate the parameters of the model with simulated maximum likelihood, and use the estimates to quantify the value of the report card information. We model both continuous specifications with Gaussian priors and signals, and discrete specifications with Beta priors and Binomial signals. We find that the release of information had a statistically significant effect on health plan choices. Consumers were willing to pay about $330 per year per below expected performance rating avoided, and the average value of the report card per employee was about $20 per year. We find large variation in valuations across different performance domains, but no significant evidence of heterogeneity based on observable employee characteristics or unobservable dimensions.
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