Association for Public Policy Analysis & Management

The Survey of Income and Program Participation (SIPP) was redesigned for the 2014 panel. With this redesign came the opportunity to use new modeling methods along with administrative records to improve imputations in the SIPP. As an initial step toward this transition, this methodology was applied to select, high-level branching variables that we have called ‘topic flags.’ Topic flags indicate whether a certain section of questions (e.g. about Social Security receipt) were relevant for a respondent. Topic flags summarize screener questions and monthly-level data to an annual indicator of employment, social insurance programs, means-tested programs, health insurance, and more. For missing data, topic flags are imputed using a parametric method called Sequential Regression Multivariate Imputation (SRMI). As opposed to hot-deck imputation that can only control for a limited number of characteristics, SRMI can control for many more variables. The variables used in the model-based imputation can also come from household, spouse, or parent characteristics. Moreover, our models include data from administrative records, which helps to mitigate the problem of survey data not “missing at random.” This paper describes our modeling process, its advantages over more traditional imputation methods like hot-deck imputation, and demonstrates the usefulness of linking administrative data into the models.