Alternatives for Modeling Results from the RAND Health Insurance Experiment


Publication Date: March 2006

Publisher: Library of Congress. Congressional Research Service


Research Area: Health



The RAND Health Insurance Experiment (HIE) was ongoing from the mid1970s to the early 1980s. Two thousand nonelderly families from six urban and rural areas were randomly assigned health insurance plans with different levels of costsharing (that is, with various levels of deductibles, coinsurance, and out-of-pocket maximums). The results from this unprecedented health insurance experiment showed that people facing higher cost-sharing (that is, they had to pay a higher proportion of total health care costs out of their own pockets) had lower health care spending than those in plans with lower cost-sharing. No similar experiment has been performed since the HIE, so it remains the epochal analysis for understanding the link between health insurance cost-sharing and total health care spending. This report examines the methods used to apply the HIE results in health policy analyses.

The key variable used to try to explain health care spending in the HIE was the plans' coinsurance -- that is, the percentage of total health care costs that the individual must pay. Understanding these results from the HIE was complicated by the fact that for each coinsurance rate, there were multiple plans, each with a different out-of-pocket maximum (although the maximum never exceeded $1,000). For example, a person may have been enrolled in the "25% coinsurance plan," but after that person had spent $1,000 (or less) out of pocket, the plan effectively became a 0% coinsurance plan. Thus, the nominal coinsurance could not be used as the sole costsharing variable for explaining the impact of cost-sharing in the HIE plans.

The HIE results have been particularly useful for policy analysts estimating what effect changes in cost-sharing might have on health care spending -- in public health insurance programs, for example. Microsimulation modeling is one tool used by health policy analysts to estimate the impact of cost-sharing changes. "Micro" refers to the fact that the modeling takes place on an individual level rather than an aggregate level, based on a database of individuals representative of a certain population (the U.S. population or a smaller subset, such as individuals enrolled in Medicaid). If one wanted to estimate the impact of an increase in coinsurance, for example, a microsimulation model would apply that increase to every person in the data along with a concomitant drop in total health care spending.

In most health insurance modeling, the HIE results remain the basis for adjusting total health care spending in response to cost-sharing changes. However, applying those results in a model is not always straightforward. One of two methods is typically used -- elasticities, generally preferred by health economists, and induction, preferred by actuaries. Each has benefits and shortfalls, but little comparative analysis has been done. This report begins by generally describing and comparing elasticities and induction factors. The report then summarizes key findings from the HIE and discusses how elasticities and induction factors can be used to replicate those results. Because of the limitations of these methods in modeling, this report offers a third alternative that appears to better replicate the HIE results. This method, called the cubic formula, is simply a formula that produces HIE-reported spending levels from the experiment's four coinsurance levels.