Matching and Cleaning Administrative Data

Robert M.Goerge and Bong Joo Lee

This paper addresses the cleaning and linking of individual-level administrative data for the purposes of social program research and evaluation. We will define administrative data as that collected in the course of programmatic activities for the purposes of client-level tracking, service provision, or decision making—essentially, nonresearch activities. Although some data sets are collected with both programmatic and research activities in mind—birth certificates are a good example—researchers usually think of administrative data as a secondary data source in contrast to surveys that are conducted solely for research purposes.

When we refer to administrative data to be used for research and evaluation of social programs, we are referring primarily to data from management information systems designed to assist in the administration of participant benefits, including, income maintenance, food stamps, Medicaid, nutritional programs, child support, child protective services, childcare subsidies, Social Security programs, and an array of social services and public health programs. Because the focus of the research is often on individual well-being, most government social programs aimed at individuals could be included.

Before these administrative data can be used for research purposes, cleaning the data is a major activity as it is in conducting and using any large social surveys. Cleaning is necessary because there are numerous sources of potential error in the data and because the data are not formatted in a way that is easily analyzed by social scientists. We take a broad view of cleaning. It is not just correcting “dirty” data; it is producing a clean data set from a messy assortment of data sets. In this paper, cleaning refers to the entire process of transforming the data as it exists in the information system into an analytic data set.

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