Econometric Modelling Using Factual Anonymized Data


Over the last decades empirical research in the social sciences showed an increasing interest in the analysis of microdata. Due to confidentiality and privacy considerations, the original data can often not be made available to the researcher. Individual and household data as well as firm level data contain sensitive information on the observational unit, whose confidentiality has to be protected against disclosure in the interest of the observational unit, but also in the interest of the data collecting institution. Private and public data collecting institutions therefore become interested in the provision of scientific-use-files that optimally combine the interests of the survey respondents, the data collecting institution and the academic user. In order to minimize the probability of disclosing individual information, statistical offices and other data collecting institutions apply various masking procedures. However, common data mashing procedures severely reduce the quality of the data by reducing the efficiency of econometric estimators and, even worse, by rendering estimators to be biased and inconsistent.

Project Description

The goal of this research project is to analyze the consequences of popular disclosure limitation methods on the properties of nonlinear estimators. This research project investigates to what extent appropriate econometric techniques can obtain parameter estimates of the true data generating process, if the data are masked by noise addition or blanking.