Simulation and quality of a synthetic close-to-reality employer–employee population

Abstract

It is of essential importance that researchers have access to linked employer-employee data, but such data sets are rarely available for researchers or the public. Even in case that survey data have been made available, the evaluation of estimation methods is usually done by complex design-based simulation studies. For this aim, data on population level are needed to know the true parameters that are compared with the estimations derived from complex samples. These samples are usually drawn from the population under various sampling designs, missing values and outlier scenarios. The structural earnings statistics sample survey proposes accurate and harmonized data on the level and structure of remuneration of employees, their individual characteristics and the enterprise or place of employment to which they belong in EU member states and candidate countries. At the basis of this data set, we show how to simulate a synthetic close-to-reality population representing the employer and employee structure of Austria. The proposed simulation is based on work of A. Alfons, S. Kraft, M. Templ, and P. Filzmoser [On the simulation of complex universes in the case of applying the German microcensus, DACSEIS research paper series No. 4, University of Tübingen, 2003] and R. Münnich and J. Schürle [Simulation of close-to-reality population data for household surveys with application to EU-SILC, Statistical Methods & Applications 20(3) (2011c), pp. 383-407]. However, new challenges are related to consider the special structure of employeremployee data and the complexity induced with the underlying two-stage design of the survey. By using quality measures in form of simple summary statistics, benchmarking indicators and visualizations, the simulated population is analysed and evaluated. An accompanying study on literature has been made to select the most important benchmarking indicators.

A systematic overview on methods to protect sensitive data provided for various analyses

“…Two examples of synthetic data that are used heavily in literature are the synthetic data of the European Statistics on Income Living Conditions (EU-SILC), produced by [2] and the synthetic version of the Structure of Earnings Survey [79]. Software for generating such synthetic data is, for example, provided by synthpop [63] for data without any of the mentioned complicated structures and by simPop [82] for more complex data sets.…”

An Empirical Study of Applying Statistical Disclosure Control Methods to Public Health Research

“…In addition, Sweeney [7] has provided a computer program named Datafly to guarantee anonymity when sharing medical data, and Berman [8] has provided a general algorithm that removes all identifiers and private information. As for generating a synthetic microdata method, readers can read Drechsler [9], Alfons et al [10], and Templ and Filzmoser [11] for more details.…”

Statistical Disclosure Control for Micro-Data Using theRPackagesdcMicro

“…One possibility is to simulate all variables of a micro-data set (i.e., simulate fully synthetic data) by statistical modeling and drawing from predictive distributions. This topic and its application in R is fully covered in Alfons et al (2011) and Templ and Filzmoser (2014) and can be practically carried out using R package simPopulation (Alfons and Kraft 2013).…”