Studies of Welfare Populations: Data Collection and Research Issues. Institutional Methods for Restricted Access

06/01/2002

If data alteration is not the final answer (and there is substantial disagreement about this given some of the technical possibilities), then some new institutional forms need to be created to protect confidentiality. Many approaches are possible, but we shall discuss two especially useful ones, research data centers and licensing combined with substantial penalties for misuse.

Research data centers. The U.S. Census Bureau has been working with other federal agencies for the past few years to create Census Research Data Centers (CRDCs) in several locations around the country (Boston, California, Chicago, Pittsburgh, and North Carolina) where researchers can go to work with nonpublic census data under strict supervision and after a stringent application process. The goal of the CRDCs is to improve the quality of census data by getting researchers to use the data in new ways that push the data to their limits. The centers are locked and are secure facilities where researchers can come to work on microdata, but only after they have developed a proposal indicating how their work will help to improve the data and signed a contract promising to meet all the obligations to protect it required of Census Bureau employees. Once they have passed these hurdles, they can work with the data in the CRDC facility, but they can only remove output once it has undergone disclosure analysis from an on-site Census Bureau employee.

The CRDC model has worked well for some innovative projects, but it has its drawbacks. It is costly, requiring several hundred thousand dollars a year to cover space, equipment, the Census Bureau employee salary, and other needs, and it is not clear how these costs can be covered in the long run even though fees have been charged to researchers. Although the CRDCs have improved access for some researchers, others still must travel some distance to the nearest site. The approval process takes time, and the outcome is uncertain. Data availability often depends on the ability of Census Bureau employees to devote time to projects that may not be their first priority. There is some concern on the part of the Census Bureau about having microdata located away from the Census Bureau itself. Universities have concerns about storing confidential data on-site.

Despite these problems, something like these centers seems to be an inevitable result of researchers' desires for data and the confidentiality concerns of the governmental agencies that own the data. In our discussion of principle 5, "A central clearinghouse negotiates or assists in legal and technical issues," we noted that organizations such as the University of Chicago's Chapin Hall, the South Carolina Budget and Control Board, and the University of Missouri at Columbia's Department of Economics are developing variants of these centers. We can imagine many different approaches to these centers depending on where they are located (state governments or universities), how they are funded, how they determine access to data, and what types of responsibilities and limitations are placed on researchers.

Licensing and increased penalties for misuse--The great drawbacks of the RDC model are the costs and the need to travel to specific locations to do research. For some data sets, another approach might make more sense. Since 1991, the National Center for Educational Statistics (NCES) has issued nearly 500 licenses for researchers to use data from NCES surveys (National Research Council, 2000:44). As part of the licensing process, researchers must describe their research and justify the need for restricted data, identify those who will have access to the data, submit affidavits of nondisclosure signed by those with this access, prepare and execute a computer security plan, and sign a license agreement binding the institution to these requirements. Criminal penalties can be invoked for confidentiality violations. This model easily could be extended to other data, and it would work especially well for discouraging disclosure matching in cases where unique identifiers, but not all key identifiers, have been removed from the data.

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