matching plays in the creation of data sets that drive microsimulation models and other analyses. Today, statistical matching seems to be the only possibility for providing information about a large number of public policy issues in the current climate of restrictive budgets, protection of data confidentiality, and reduction of respondent burden. It is easy to dismiss statistical matching as a technique that makes unsupported assumptions about the covariance of YZ, which is of primary interest. However, rather than dismiss statistical matching, we need to consider ways in which it can be improved, at least until the climate changes. In the discussion above, some techniques have been offered, especially the use of sensitivity analyses and the use of auxiliary information, that might be available from small special studies, including small exact matches or small comprehensive surveys, or through the use of census information, from which the degree of conditional dependence can be estimated. (For a thorough discussion of ways in which auxiliary information might be used, see Singh et al. [1990].) The increased use of sensitivity analysis would indicate the degree to which inferences might be compromised by the failure of the conditional independence assumption. The increased use of auxiliary information would permit the creation of data sets that better mimic the properties of the data set needed for analysis, and would likely facilitate a great deal of useful policy analysis that would be impossible today without this type of statistical matching. Armstrong (1989:44) states:
The study also suggests…that auxiliary information about the distribution of (Y, Z) (obtained, for example, from a sample of (X, Y, Z) observations) is necessary to reduce distortion in the conditional distribution of (Y, Z) given X.
Such an approach is likely to be able to better guide statistical matching.
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