Abstract
The production of synthetic datasets has been proposed as a statistical disclosure control solution to generate public use files out of protected data, and as a tool to create "augmented datasets" to serve as input for micro-simulation models. Synthetic data have become an important instrument for ex-ante assessments of policy impact. The performance and acceptability of such a tool relies heavily on the quality of the synthetic populations, i.e., on the statistical similarity between the synthetic and the true population of interest. Multiple approaches and tools have been developed to generate synthetic data. These approaches can be categorized into three main groups: synthetic reconstruction, combinatorial optimization, and model-based generation. We provide in this paper a brief overview of these approaches, and introduce simPop, an open source data synthesizer. simPop is a user-friendly R package based on a modular object-oriented concept. It provides a highly optimized S4 class implementation of various methods, including calibration by iterative proportional fitting and simulated annealing, and modeling or data fusion by logistic regression. We demonstrate the use of simPop by creating a synthetic population of Austria, and report on the utility of the resulting data. We conclude with suggestions for further development of the package.
Highlights
Recent years have seen a considerable increase in the production of socio-economic data and their accessibility by researchers
While iterative proportional updating (IPU) has a significant advantage over iterative proportional fitting (IPF) in that it allows simultaneous matching of household- and individual level attributes, it is obvious that the total number of constraint totals in the joint distributions is important with respect to the quality of the final weights
We demonstrate how simPop can be used to generate a synthetic population of Austria, using publicly available survey microdata and tabulated census data
Summary
Recent years have seen a considerable increase in the production of socio-economic data and their accessibility by researchers. Privacy protection principles and regulations impose restrictions to access and use of individual data, and standard statistical disclosure control methods do not always suffice to protect the confidentiality of the data It is increasingly becoming the exception rather than the rule that one specific dataset meets all the needs of the analyst. Synthetic data generation allows the creation of new, richer or “augmented” datasets that provide critical input for micro-simulation (including spatial micro-simulation) and agentbased modeling. Such datasets are appealing for policymakers and development practitioners, who use them as input into simulation models for assessing the ex-ante distributional impact of policies and programs.
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