Changes in the Austrian Structure of Wages, 1996-2002: Evidence from Linked Employer-Employee Data

Pointner, Wolfgang; Stiglbauer, Alfred

doi:10.2139/ssrn.1708129

Cited by 1 publication

(4 citation statements)

References 12 publications

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“…The microdata group pursues three directions of research one of which is on wage differentials and modelling of earnings. The SES data is one of the main data sources for this group, used by many authors (see, e.g., Caju et al 2010Caju et al , 2009aMessina et al 2010;Dybczak and Galuscak 2010;Simo ´n 2010;Pointner and Stiglbauer 2010).…”

Section: Access To Ses Microdata and European Projectsmentioning

confidence: 99%

“…The topic Women and Equality is of central interest not only for the Federal Minister for Women and the Civil Service, and socioeconomic studies are carried out with support from the state (one example is Geissberger 2010) or European institutions where regression models are also applied to estimate the adjusted gender pay gap (Research Center for Education and the Labour Market at the Maastricht University 2009). Wage differentials and interindustry wage differentials: Differences in earnings for workers employed in different industries and occupations has long been recognised as an important issue for the labour market and several studies have been carried out (Caju et al 2010(Caju et al , 2009aMessina et al 2010;Dybczak and Galuscak 2010;Simo ´n 2010;Pointner and Stiglbauer 2010). Pointner and Stiglbauer (2010) use several workplacespecific dummy variables for the employee's occupation (ISCO 1) within the firm, the sector (NACE-2 digits) of the employer, for firm size and location (NUTS-1 digits), and a control for private ownership of the firm as predictors.…”

Section: Summary Of the Most Important Analyses From Ses Datamentioning

confidence: 99%

“…Wage differentials and interindustry wage differentials: Differences in earnings for workers employed in different industries and occupations has long been recognised as an important issue for the labour market and several studies have been carried out (Caju et al 2010(Caju et al , 2009aMessina et al 2010;Dybczak and Galuscak 2010;Simo ´n 2010;Pointner and Stiglbauer 2010). Pointner and Stiglbauer (2010) use several workplacespecific dummy variables for the employee's occupation (ISCO 1) within the firm, the sector (NACE-2 digits) of the employer, for firm size and location (NUTS-1 digits), and a control for private ownership of the firm as predictors. Caju et al (2010Caju et al ( , 2009b modelled the gross hourly wages with sex, education, age class, number of years of employment, type of employment contract, part/full-time, bonus for shift work, night and/or weekend work, a dummy for paid overtime and occupation sector effect.…”

Section: Summary Of the Most Important Analyses From Ses Datamentioning

confidence: 99%

“…The data from the Lissy system is also used for the LEED project (see Subsection 3.3) where similar studies and modelling have been carried out (see, e.g., Simo ´n 2010). Similar models are also fitted within the wage dynamics network of the European Central Bank (Caju et al 2010;Pointner and Stiglbauer 2010).…”

Section: Model-based Predictions On Employment Levelmentioning

confidence: 99%

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Quality Indicators for Statistical Disclosure Methods: A Case Study on the Structure of Earnings Survey

Templ

2015

Journal of Official Statistics

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1Scientific-or public-use files are typically produced by applying anonymisation methods to the original data. Anonymised data should have both low disclosure risk and high data utility.Data utility is often measured by comparing well-known estimates from original data and anonymised data, such as comparing their means, covariances or eigenvalues.However, it is a fact that not every estimate can be preserved. Therefore the aim is to preserve the most important estimates, that is, instead of calculating generally defined utility measures, evaluation on context/data dependent indicators is proposed.In this article we define such indicators and utility measures for the Structure of Earnings Survey (SES) microdata and proper guidelines for selecting indicators and models, and for evaluating the resulting estimates are given. For this purpose, hundreds of publications in journals and from national statistical agencies were reviewed to gain insight into how the SES data are used for research and which indicators are relevant for policy making.Besides the mathematical description of the indicators and a brief description of the most common models applied to SES, four different anonymisation procedures are applied and the resulting indicators and models are compared to those obtained from the unmodified data. The disclosure risk is reported and the data utility is evaluated for each of the anonymised data sets based on the most important indicators and a model which is often used in practice.

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