How the RAND COMPARE Microsimulation Model Works

RAND researchers developed the COMPARE microsimulation model as a way of projecting how households and firms would respond to health care policy changes based on economic theory and existing evidence from smaller-scale changes (e.g., changes in Medicaid eligibility). A microsimulation model uses computer software to develop a synthetic U.S. population made up of individuals, families, firms, and the federal and state governments.

Individuals, firms, and other agents (the general name given to entities that can take actions) in our model make decisions using a customized “rule book,” which takes into account such factors as individual and family characteristics, prices, and government regulations. For example, if an offer is available, an individual in our model would make the choice to enroll in employer sponsored health insurance or not after considering the following:

  • Whether he or she was eligible for other options, such as Medicaid
  • The cost of employer-sponsored insurance, overall and relative to other options
  • Individual characteristics, such as total family income and health
  • Whether the government offered an incentive to enroll in insurance, such as a tax credit, or a penalty for non-enrollment.

The individual’s decision in the status quo might change after a policy intervention. For example, a person who declines employer-sponsored insurance in the status quo might opt to enroll in an insurance plan if the government introduced an individual mandate with a substantial non-enrollment penalty. Firms in our model also follow a rule book, opting to offer health insurance after considering the value of insurance as a recruitment and retention tool, the expected cost of offering a policy, and any government regulations that might provide an incentive or disincentive to offer insurance.

An advantage of the microsimulation approach is that it allows us to incorporate interactions among agents (firms, households, and the government) in the model. For example, a Medicaid expansion might cause some newly eligible workers to drop employer-sponsored health insurance in favor of public coverage. Employers in our model can respond to this behavior by reassessing the benefit of providing health insurance to workers. If a substantial share of workers becomes newly eligible for Medicaid, then the firm may decide to stop offering insurance. Similarly, an employer mandate that imposes a penalty on non-offering firms may cause some businesses to begin offering health insurance. In response, some workers in these firms might opt to take employer coverage.

The first step in using the microsimulation is to compute the status quo--the way things now stand. It is crucial that the status quo configuration provide a realistic picture of the U.S. population at a point in time. For example, insurance premiums predicted by the model must match observed premiums with reasonable accuracy.

The second step is using the model to simulate a policy option. We simulate policy options by altering the values of appropriate attributes (e.g., health insurance premiums, regulatory requirements, worker preferences) and allowing the agents to respond to these changes and settle into a new equilibrium. We can then compute the outcome of the policy option by comparing the new equilibrium with the status quo. The model not only predicts the effect of various health policy options on spending, coverage, and health outcomes, but it also predicts how specific design features influence the effects of a policy option. For example, depending on the magnitude of the noncompliance penalty and the degree to which small firms are excluded from the mandate, an employer mandate may have a very different effect on health insurance coverage.

Data for developing the model population and predicting household and firm behavior come from nationally representative surveys conducted by government agencies and private foundations. Key data sources used in the model include the Survey of Income and Program Participation (SIPP), the Medical Expenditure Panel Survey (MEPS), the Kaiser Family Foundation/Health Research and Educational Trust Employer Survey (Kaiser/HRET), and the Survey of U.S. Businesses (SUSB). We also draw from published literature, as well as from documentation published by other modelers--most especially, by Jonathan Gruber of MIT and the Urban Institute.

Specific details about how the model works can be found in Appendix A of our report on establishing state health insurance exchanges and Appendix D of our report on employer self-insurance decisions after the ACA takes full effect.