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The existence of SIPP public use files poses a key challenge for disclosure avoidance. To protect the confidentiality of survey respondents, it was deemed necessary to prevent reidentification of a record that appears in the synthetic data against the existing SIPP public use files. Hence, all information regarding the dating of variables whose source was a SIPP response, and not administrative data, has to be made consistent across individuals regardless of the panel and wave from which the response was taken. The public use file contains several variables that were never missing and are not synthesized. These variables are: gender, marital status, spouse’s gender, initial type of Social Security benefits, type of Social Security benefits in 2000, and the same benefit type variables for the spouse. All other variables in the SSB v4 were synthesized.

The model first imputes any missing data, then synthesizes the completed data (Reiter 2004). For each iteration of the missing data imputation phase and again during the synthesis phase, a joint PPD for all of the required variables is estimated according to the following protocol. At each node of the parent/child tree, a statistical model is estimated for each of the variables at the same level. The statistical model is a Bayesian bootstrap, logistic regression, or linear regression (possibly with transformed inputs). The missing data phase included nine iterations of estimation. The synthetic data phase occurred on the 10th iteration. Four missing data implicates were created. These constitute the completed data files that are the inputs to the synthesis phase. Four synthetic implicates were created for each missing data implicate, for a total of 16 synthetic implicates on the released file. Because copying the final weight to each implicate of the synthetic data would have provided an additional unsynthesized variable with 55 552 distinct values, the disclosure risk associated with the weight variable had to be addressed. A synthetic weight using a PPD based on the Multinomial/Dirichlet natural conjugate likelihood and prior was created.