committee looks forward to analyzing more detailed data from these projects in the future, including information on the fields they are matching, the number of potential duplicates on the lists, and the number of actual duplicates they remove from their lists. A start at tracking some efforts at interstate checking of duplicate registrations can be found in the EAC report Impact of the National Voter Registration Act on Federal Elections 2005-2006.6 On page 76 of that report can be found the fact that at least three groups of states have checked for such duplicates at least once: District of Columbia, Virginia, and Maryland; Minnesota, Missouri, Nebraska, Kansas, and Iowa; and Kentucky, South Carolina, and Tennessee.

Improving match accuracy can contribute to improved completeness of a VRD. Match accuracy, whether performed by automated processes or manual review, can be benefited by tertiary, third-party, data. When such external data are carefully harnessed for improved match accuracy, systems can more often resolve ambiguities without human involvement. Reducing the number of exceptions necessitating human review and judgment increases the repeatability of list maintenance.

Such data can be used in two ways. First, such data can be acquired across the entire population and made available for error-correction processes. Second, data can be selectively made available only when they are needed to resolve ambiguities in any putative record-level match—an approach that minimizes privacy concerns because it obtains additional data on individuals only when they are needed.7

When using third-party data to enhance matching accuracy, additional logging and accountability requirements must be introduced. Each third-party record requested and received must be retained and retained in its original form until it is no longer needed (for example, until the point that the voter has confirmed any changes that may have resulted from the use of such data). Furthermore, any third-party record used to improve a match should be logged and accounted for similarly. In addition, government matching with third-party datasets raises privacy concerns (such as concerns if credit header data is merged with voter history data, for example).


Application of the techniques discussed above is intended to improve the quality of the data in a VRD by making the data more accurate—that is, these techniques allow erroneous data to be changed into correct data. But their success in doing so is not guaranteed—use of the techiques may introduce additional error, or the original data may in fact have been correct. Thus, it may well be advisable to keep the old data as well as the new, but with a flag that indicates that the old data have been corrected. In addition, a policy must be established regarding notification of the voter if a field is changed. The cost of such notification must be weighed against the value of ensuring with high confidence that the updated data are correct.


Available at See also Thad Hall and Michael Alvarez, “The Next Big Election Challenge: Developing Electronic Data Transaction Standards for Election Administration,” IBM Center for the Business of Goverment, 2005, available at


This technique is explained in detail in Paul Rosenzweig and Jeff Jonas, “Correcting False Positives: Redress and the Watch List Conundrum,” Legal Memorandum 17, The Heritage Foundation, June 17, 2005, available at

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