Remote Access. Eine Welt ohne Mikrodaten ?? - Gerd Ronning; Philipp Bleninger; Jörg Drechsler; Christopher Gürke (Verein für Socialpolitik, 2010)
Use of microdata is severely hampered in many areas of research. This is in particular true for data from statistical offices. One way to circumvent this problem is to anonymize the data such that both confidentiality is guaranteed and informational content of the data is not to much distorted by the anonymization procedure. However many researchers prefer the use of 'original' data. Therefore in recent years remote access/execution ('Fernrechnen') has become quite popular where the original micro data are used in the statistical analysis but are not available to the researchers. Clearly, this alternative takes more time since program files have to be sent to the statistical office. However, the euphoria for this approach has cooled down a bit since it has become apparent that here also problems of confidentiality exist. Most obvious is the fact that residuals cannot be provided. See, for example, Gomatam et al. (2005). However, there are very different kinds of 'disclosures' which are discussed in the paper. The paper also draws attention to the use of saturated models which bear the risk of reproducing confidential tabular data. Analysis of variance is the relevant tool in reproducing magnitude tables whereas the corresponding micro-econometric models can be used to reproduce frequency tables: Logit models give the results in case of a nominal variable and Poisson regression is the approach in case of count data. We also shortly discuss possible disclosure risk in the standard multivariate procedures (factor analysis, principal components, cluster analysis and multidimensional scaling). It is clear from the many examples given in the paper that the remote access/execution option will ask for a large amount of statistical expertise in the statistical office in order to check for disclosure risk. Additionally, there will be a tendency not to provide statistical results to the researcher if critical variables such as region or sector are demanded as regressors in the program file. Perhaps a much cruder classification of regions and sectors will be allowed which in a way is the situation used in providing anonymized data.
Keywords: C00; C81; C49; 330; Remote execution; confidential tabular data; saturated models; disclosure risk; artificial outlier; strategic dummy variable; cluster analysis; factor analysis; MDS