[Zurück]

M. Templ, P. Filzmoser:
"Simulation and quality of a synthetic close-to-reality employer-employee population";
Journal of Applied Statistics, 41 (2014), 5; S. 1053 - 1072.

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 [{\em 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 [{\em 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 employer-employee 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.

employer-employee data, synthetic data simulation, statistical modeling, benchmarking indicators

http://dx.doi.org/10.1080/02664763.2013.859237

http://www.tandfonline.com/doi/full/10.1080/02664763.2013.859237#.Uqb21Kqnd3C