Creating Improved Survey Data Products Using Linked Administrative-Survey Data

Publication Date:
Sep 2019

Recent research linking administrative to survey data has laid the groundwork for improvements in survey data products. However, the opportunities have not been fully realized yet. In this article, the authors' main objective is to use administrative-survey linked microdata to demonstrate the potential of data linkage to reduce survey error through model-based blended imputation methods. Theyuse parametric models based on the linked data to create imputed values of Medicaid enrollment and food stamp (SNAP) receipt. This approach to blending data from surveys and administrative data through models is less likely to compromise confidentiality or violate the terms of the data sharing agreements among the agencies than releasing the linked microdata, and they demonstrate that it can yield substantial improvements of estimate accuracy. Using the blended imputation approach reduces root mean squared error (RMSE) of estimates by 81 percent for state-level Medicaid enrollment and by 93 percent for substate area SNAP receipt compared with estimates based on the survey data alone. Given the high level of measurement error associated with these important programs in the United States, data producers should consider blended imputation methods like the ones described in this article to create improved estimates for policy research.


RSF: The Russell Sage Foundation Journal of the Social Sciences is a peer-reviewed, open-access journal of original empirical research articles by both established and emerging scholars.


The Russell Sage Foundation offers grants and positions in our Visiting Scholars program for research.


Join our mailing list for email updates.