  Data holders can produce synthetic versions of datasets when concerns about
potential disclosure restrict the availability of the original records. This
paper is concerned with methods to judge whether such synthetic data have a
distribution that is comparable to that of the original data, what we will term
general utility. We consider how general utility compares with specific
utility, the similarity of results of analyses from the synthetic data and the
original data. We adapt a previous general measure of data utility, the
propensity score mean-squared-error (pMSE), to the specific case of synthetic
data and derive its distribution for the case when the correct synthesis model
is used to create the synthetic data. Our asymptotic results are confirmed by a
simulation study. We also consider two specific utility measures, confidence
interval overlap and standardized difference in summary statistics, which we
compare with the general utility results. We present two examples examining
this comparison of general and specific utility to real data syntheses and make
recommendations for their use for evaluating synthetic data.
