incremental value to them (the incremental value is the positive difference between their marginal out-of-pocket price and the marginal cost of those resources in the health care market). This additional care has some benefit, but must be paid for out-of-pocket or by the insurance pool’s premiums without a compensating change in premiums to that patient or family, or by compensating changes in take-home income/wages.

To incorporate these effects, the sample would need to be partitioned into large insurance categories, such as uninsured, Medicaid, health maintenance organization, private insurance (a few types), Medicare, etc. Under the ACA, it would be useful to distinguish between the various insurance levels (bronze, etc.). For private insurance, if one has details on the actual premiums and coverage provisions, one may standardize by adjusting total spending and out-of-pocket spending from actual to a standard, using the details of insurance to let the coinsurance rate at the time of spending affect the quantity of care obtained and thus out-of-pocket spending. Eventually, one will have to decide how to adjust uninsured spending to what it would have been if the individual were in Medicaid or a standard private policy and other possible policy shifts, such as from a private policy to being uninsured. One can calculate the range of spending for uninsured people either by looking at unadjusted spending in cells of uninsured people, and then adjusting later for their getting a different insurance policy, or by adjusting to a standard policy before grouping cases into cells and predicting adjusted spending, which is then adjusted back from the standard to their coverage in each policy simulation.


We conclude by illustrating the usefulness of a measure of MCER. Figure 4-1 shows the probability of out-of-pocket medical care spending exceeding the difference between family income and the SPM target threshold as a function of the family’s ratio of income to the SPM target. These probabilities depend on income, health, and the age composition of families; the graph looks like a survival curve if one goes from very low to high income. If there were a set of results from before the ACA was implemented and one after 2014 when it is largely implemented, as Figure 4-2 illustrates, then the area between the pre- and post-curves would become one measure of improvement. The usual caveat about confounded changes in a before-and-after study (for example, the global recession), applies: Both spending and the SPM depend on these external factors. In principle, however, one can simply label each family by its income compared with the SPM target and calculate the probability that out-of-pocket medical care spending will take it below the target. In Figure 4-2, the curved black line is

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