does not collect that information. For example, to use MEPS and then glue that health information onto the CPS, what is involved?
Czajka responded that this general approach is what is used for the SPM and the experimental measures, and recently only the CPS has started collecting these other components. The approach has relied on these kinds of methods, imputing for the expenditures largely from MEPS and also maybe SIPP. It is done in microsimulation models and clearly has risks. One is trying to match, and making assumptions that the variables that one is not matching on are lining up with the variables that one is matching. It is not perfect. He said he thought that, down the road, they would probably have to think about expanding the CPS, if that is the source, to add other measures, and hope for the same success that the Bureau has had with the medical out-of-pocket expenditures.
Pamela Short asked if the CPS has a scale for excellent, very good, good, fair, and poor health. Bringing that into the statistical matching would generate a lot more confidence than without that variable.
Sarah Meier commented that, with respect to the conceptual model, the risk adjustment models explain only a relatively small amount of the variation in health expenditures. So working with a very complex model that includes ICD-9 diagnosis codes and all sorts of other information, is actually not going to be a big improvement from working with just a scale of poor, good, and very good health status. Stepping back from the idealized version of what one might want to do if there were no data limitations, the types of variables in terms of health status that are available in the CPS would be a reasonable base for the health characteristic cells of interest here.
The bigger issue would be the expenditure information, how strong and solid that is in modeling of the actual expenditure distributions. So one could think of modeling those in MEPS and then attaching that to the CPS. The big issue with implementing the full type of model is the lack of insurance characteristics in the CPS.
Banthin cautioned that she would be concerned about matching MEPS to the CPS. The out-of-pocket spending measure is just out-of-pocket, it is not total, she said. And there are those extra zeros. It involves the entire distribution.
And just like the orthogonality of assets to income, it is important to preserve the orthogonality of expenditures, even the distribution of medical expenditures to income or to out-of-pocket. It is a different dimension.
Also, a scale for excellent, very good, fair, and poor health is a great predictor, but additional data are needed. Although she has built many a simulation model in which expenditures are matched to others, that was for simulating changes in policy. This is for the construction of a medical care risk index. So it does concern her that one of the key variables would be imputed with only a limited set of matching covariates.