[Alfons, A. and M. Templ. 2013. “Estimation of Social Exclusion Indicators from Complex Surveys: The R package laeken.” Journal of Statistical Software 54: 1–25.10.18637/jss.v054.i15]Search in Google Scholar
[Alfons, A., S. Kraft, M. Templ, and P. Filzmoser. 2011. “Simulation of Close-to-Reality Population Data for Household Surveys with Application to EU-SILC.” Statistical Methods & Applications 20: 383–407. doi:10.1007/s10260-011-0163-2.10.1007/s10260-011-0163-2]Search in Google Scholar
[Alfons, A., M. Templ, and P. Filzmoser. 2013. “Robust Estimation of Economic Indicators from Survey Samples Based on Pareto Tail Modeling.” Journal of the Royal Statistical Society Series C 62: 271–286.10.1111/j.1467-9876.2012.01063.x]Search in Google Scholar
[Beblot, M., D. Beniger, A. Heinze, and F. Laisney. 2003. Methodological Issues Related to the Analysis of Gender Gaps in Employment, Earnings and Career Progression. Final Project Report, European Commission Employment and Social Affairs DG.]Search in Google Scholar
[Belfield, R. 1999. Pay Inequalities and Economic Performance: A Review of the UK Literature. Technical Report PiEP Report, Centre for Economic Performance, London School of Economics.]Search in Google Scholar
[Bowles, S., H. Gintis, and M. Osborne. 2001. “The Determinants of Earnings: a Behavioral Approach.” Journal of Economic Literature 39: 1137–1176.10.1257/jel.39.4.1137]Search in Google Scholar
[Brand, R. 2004. “Microdata Protection through Noise Addition.” In Privacy in Statistical Databases. Lecture Notes in Computer Science, edited by J. Domingo-Ferrer. 347–359. New York: Springer.]Search in Google Scholar
[Bruch, C., R. Münnich, and S. Zins. 2011. Variance Estimation For Complex Surveys. Research Project Report WP3–D3.1, FP7-SSH-2007-217322 AMELI. Available at: http://ameli.surveystatistics.net (accessed December 2013)]Search in Google Scholar
[Caju, P., C. Fuss, and L. Wintr. 2009a. “Understanding Sectoral Differences in Downward Real Wage Rigidity: Workforce Composition, Institutions, Technology and Competition.” Working Paper Series no. 1006, European Central Bank. Available at: http://www.ecb.int/pub/pdf/scpwps/ecbwp1006.pdf (accessed December 2013)]Search in Google Scholar
[Caju, P., F. Rycx, and I. Tojerow. 2009b. “Inter-industry Wage Differentials: How Much Does Rent Sharing Matter?” Journal of the European Economic Association 79: 691–717.10.1111/j.1467-9957.2010.02173.x]Search in Google Scholar
[Caju, P., F. Rycx, and I. Tojerow. 2010. “Wage Structure Effects of International Trade: Evidence From a Small Open Economy.” Working Paper Series no. 1325, European Central Bank. Available at: http://www.ecb.int/pub/pdf/scpwps/ecbwp1325.pdf (accessed December 2013)]Search in Google Scholar
[Carlson, M. 2002. “Assessing Microdata Disclosure Risk Using the Poisson-inverse Gaussian Distribution.” Statistics in Transition 5: 901–925.]Search in Google Scholar
[Casali, S. and V. Alvarez. 2010. 17% of Full-time Employees In the EU Are Low-wage Earners. Statistics in focus. Research Report. KS-SF-10-003-EN-N, Eurostat/European Commission. Available at: http://epp.eurostat.ec.europa.eu/cache/ITY_OFFPUB/KS-SF-10-003/EN/KS-SF-10-003-EN.PDF (accessed December 2013)]Search in Google Scholar
[Dell’Aringa, C., P. Ghinetti, and C. Lucifora. 2000. “Pay Inequality and Economic Performance in Italy: a Review of the Applied Literature.” In Proceedings of the LSE conference, November 3–4, 2000. 1–28. London.]Search in Google Scholar
[Deville, J.-C. and C.-E. Särndal. 1992. “Calibration Estimators in Survey Sampling.” Journal of the American Statistical Association 87: 376–382.10.1080/01621459.1992.10475217]Search in Google Scholar
[Deville, J.-C., C.-E. Särndal, and O. Sautory. 1993. “Generalized Raking Procedures in Survey Sampling.” Journal of the American Statistical Association 88: 1013–1020.10.1080/01621459.1993.10476369]Search in Google Scholar
[Domingo-Ferrer, J. and V. Torra. 2001. “A Quantitative Comparison of Disclosure Control Methods for Microdata.” Confidentiality, Disclosure and Data Access: Theory and Practical Applications for Statistical Agencies, edited by P. Doyle, J. Lane, J. Theeuwes, and L. Zayatz. 111–134, Eurostat.]Search in Google Scholar
[Domingo-Ferrer, J., J.M. Mateo-Sanz, and T. Torra. 2001. “Comparing sdc Methods for Microdata on the Basis of Information Loss and Disclosure.” Proceedings of ETKNTTS 2001: Eurostat, Luxembourg June 18–20, 2001. 807–826. Luxembourg: Eurostat.]Search in Google Scholar
[Dupray, D., H. Nohara, and P. Béret. 1999. Pay Inequality and Economic Performance: a Review of the French Literature. Technical Report PiEP Report, Centre for Economic Performance, London School of Economics]Search in Google Scholar
[Dupré, D. 2010. “The Unadjusted Gender Pay Gap in the European Union.” In Joint UNECE/Eurostat Work Session on Gender Statistics, Geneva April 14–16, 2010. Available at: http://www.unece.org/fileadmin/DAM/stats/documents/ece/ces/ge.30/2010/1.e.pdf (accessed November 2015)]Search in Google Scholar
[Dybczak, K., and K. Galuscak. 2010. “Changes in the Czech Wage Structure: Does Immigration Matter?” Working Paper Series no. 1242, European Central Bank. Available at: http://www.ecb.int/pub/pdf/scpwps/ecbwp1242.pdf (accessed December 2013)10.2139/ssrn.1671612]Search in Google Scholar
[Edwards, C. 2010. “Public Sector Unions and the Rising Costs of Employee Compensation,” Cato Journal 30: 87–115.]Search in Google Scholar
[Efron, B. and R.J. Tibshirani. 1993. An Introduction to the Bootstrap. New York: Chapman & Hall.10.1007/978-1-4899-4541-9]Search in Google Scholar
[EU-SILC. 2004. Common Cross-sectional EU Indicators Based on EU-SILC: the Gender Pay Gap. EU-SILC 131-rev/04, Working Group on Statistics on Income and Living Conditions (EU-SILC). Luxembourg: Eurostat.]Search in Google Scholar
[EU-SILC. 2009. Algorithms to Compute Social Inclusion Indicators Based On EU-SILC and Adopted under the Open Method of Coordination (OMC). EU-SILC LCILC/39/09/ENrev.1, Directorate F: Social and Information Society Statistics Unit F-3: Living Conditions and Social Protection, European Commission. Luxembourg: Eurostat.]Search in Google Scholar
[Fitzenberger, B., K. Kohn, and A. Lembcke. 2006. Union Wage Effects in Germany: Union Density Or Collective Bargaining Coverage? Research Report FSP 1169, DFG research programme, The London School of Economics and Political Sciences, London.]Search in Google Scholar
[Franconi, L. and S. Polettini. 2004. “Individual Risk Estimation in μ-Argus: a Review.” In Privacy in Statistical Databases: Lecture Notes in Computer Science, edited by J. Domingo-Ferrer. 262–272. New York: Springer.10.1007/978-3-540-25955-8_20]Search in Google Scholar
[Franconi, L., D. Ichim, and M. Templ. 2011. First Steps to Define a Framework For Comparable Dissemination of the European Structure of Earning Survey. Deliverable d1.1-a. Task 1: Harmonisation of Microdata Release in Multiple Countries. Essnet Project on Common Tools and Harmonised Methodologies for SDC in the ESS. Available at: http://neon.vb.cbs.nl/casc/..%5Ccas%5CESSNet2%5Cdeliverable%201%20full%20august2012.pdf (accessed November 2015)]Search in Google Scholar
[Frick, B., and K. Winkelmann. 1999. Pay Inequalities and Economic Performance: A Review in Literature, Technical Report Research Report HPSE-CT-1999-00040, Ernst-Moritz-Arndt-Universität Greifswald.]Search in Google Scholar
[Geissberger, T. 2009. Verdienststrukturerhebung 2006, Struktur und Verteilung der Verdienste in Oösterreich. Vienna: Statistik Austria.]Search in Google Scholar
[Geissberger, T. 2010. Frauenbericht. Teil 4: Sozioökonomische Studien, Technical Report 4, Federal Ministry for Women and the Civil Service of Austria.]Search in Google Scholar
[Geissberger, T. and K. Knittler. 2010. “Niedriglöhne und Atypische Beschäftigung in Österreich.” Statistische Nachrichten 6: 448–461.]Search in Google Scholar
[Gini, C. 2012. “Variabilità e Mutabilità: Contributo Allo Studio delle Distribuzioni e delle Relazioni Statistiche.” Studi Economico-Giuridici della R. Università di Cagliari 3: 3–159.]Search in Google Scholar
[Gomatam, S. and A. Karr. 2003. Distortion Measures for Categorical Data Swapping. Report no. 131, National Institute of Statistical Sciences (NISS).]Search in Google Scholar
[Gouweleeuw, J., P. Kooiman, L. Willenborg, and P-P. De Wolf. 1998. “Post Randomisation for Statistical Disclosure Control: Theory and Implementation.” Journal of Official Statistics 14; 463–478.]Search in Google Scholar
[Graf, M., A. Alfons, C. Bruch, P. Filzmoser, B. Hulliger, R. Lehtonen, B. Meindl, R. Münnich, T. Schoch, M. Templ, M. Valaste, A. Wenger, and S. Zins. 2011. State-of-the-art of laeken Indicators. Research Project Report WP1–D1.1, FP7-SSH-2007-217322 AMELI. Available at: http://ameli.surveystatistics.net (accessed December 2013)]Search in Google Scholar
[Groshen, E. 1991. “The Structure of the Female/Male Wage Differential.” Journal of Human Resources 26: 455–472.]Search in Google Scholar
[Hundepool, A., J. Domingo-Ferrer, L. Franconi, S. Giessing, E. Schulte Nordholt, K. Spicer, and P.-P. de Wolf. 2012. Statistical Disclosure Control. New York: Wiley.10.1002/9781118348239]Search in Google Scholar
[Ichim, D. and L. Franconi. 2007. “Disclosure Scenario and Risk Assessment: Structure of Earnings Survey.” In Joint UNECE/Eurostat Work Session on Statistical Data Confidentiality, Manchester, December 17–19, 2007. Doi: 10.2901/Eurostat.C2007.004]Search in Google Scholar
[Ichim, D. and L. Franconi. 2010. “Strategies to Achieve sdc Harmonisation at European Level: Multiple Countries, Multiple Files, Multiple Surveys.” Privacy in Statistical Databases ‘10, edited by J. Domingo-Ferrer and E. Kajkos, Springer, New York. 284–296.]Search in Google Scholar
[Karr, A.F., C.N. Kohnen, A. Oganian, J.P. Reiter, and A.P. Sanil. 2006. “A Framework for Evaluating the Utility of Data Altered to Protect Confidentiality.” The American Statistician 60: 224–232. Doi: 10.1198/000313006X124640.10.1198/000313006X124640]Search in Google Scholar
[Kolb, J.-P., R. Münnich, S. Beil, A. Chatziparadeisis, and J. Seger. 2011. Policy Use of Indicators on Poverty and Social Exclusion. Research Project Report WP9–D9.1, FP7-SSH-2007-217322 AMELI, 2011. Available at: http://ameli.surveystatistics.net (accessed December 2013)]Search in Google Scholar
[Lorenz, M.O. 1905. “Methods of Measuring the Concentration of Wealth.” Publications of the American Statistical Association 9: 209–219.10.2307/2276207]Search in Google Scholar
[Manning, A.M., D.J. Haglin, and J.A. Keane. 2008. “A Recursive Search Algorithm For Statistical Disclosure Assessment.” Data Mining and Knowledge Discovery 16: 165–196. Doi: 10.1007/s10618-007-0078-6.10.1007/s10618-007-0078-6]Search in Google Scholar
[Marsden, D. 2010. Pay Inequalities and Economic Performance, Technical Report PiEP Final Report V4, Centre for Economic Performance, London School of Economics. London: London School of Economics. Available at: http://www.ist-world.org/ProjectDetails.aspx?ProjectID=fa5bb4adfff74d60aeca90b56441a601&SourceDatabaseID=9cd97ac2e51045e39c2ad6b86dcelac2.]Search in Google Scholar
[Messina, J., M. Izquierdo, P. Caju, C.F. Duarte, and N.L. Hanson. 2010. “The Incidence of Nominal and Real Wage Rigidity: an Individual-based Sectoral Approach.” Journal of the European Economic Association 8: 487–496.10.1111/j.1542-4774.2010.tb00519.x]Search in Google Scholar
[Mittag, J. 2005. Gross Earnings In Europe. Main Results of the Structure of Earnings Survey 2002. Statistics in Focus. Research Report. KS-NK-05-012-EN-N, European Communities. Available at: http://epp.eurostat.ec.europa.eu/cache/ITY_OFFPUB/KS-NK-05-012/EN/KS-NK-05-012-EN.PDF (accessed December 2013)]Search in Google Scholar
[Muralidhar, K. and R. Sarathy. 2006. “Data Shuffling – a New Masking Approach for Numerical Data.” Management Science 52: 658–670.10.1287/mnsc.1050.0503]Search in Google Scholar
[Nolan, B. and H. Russel. 2001. Pay Inequality and Economic Performance In Ireland: a Review of the Applied Literature. Technical Report PiEP Report, The Economic and Social Research Institute, Dublin.]Search in Google Scholar
[Oganian, A. and A.F. Karr. 2006. “Combinations of sdc Methods for Microdata Protection.” In Privacy in Statistical Databases, edited by J. Domingo-Ferrer and L. Franconi. 102–113. Berlin: Springer. Doi: 10.1007/11930242_10.10.1007/11930242_10]Search in Google Scholar
[Pointner, W., and A. Stiglbauer. 2010. “Changes In the Austrian Structure of Wages.” Working Paper Series no. 1268, European Central Bank. Available at: http://www.ecb.int/pub/pdf/scpwps/ecbwp1268.pdf (accessed December 2013)]Search in Google Scholar
[Reiter, J.P. 2012. “Statistical Approaches to Protecting Confidentiality For Microdata and their Effects on the Quality of Statistical Inferences.” Public Opinion Quarterly 76: 163–181. Doi: 10.1093/poq/nfr058.10.1093/poq/nfr058]Search in Google Scholar
[Research Center for Education and the Labour Market at Maastricht University. 2009. “Development of Econometric Methods to Evaluate the Gender Pay Gap Using Structure of Earnings Survey Data.” Research paper no. ks-ra-09-011-en-n, European Commission. Available at: http://www.ecb.int/pub/pdf/scpwps/ecbwp1006.pdf (accessed December 2013)]Search in Google Scholar
[Reuter, W. 2010. Establishing an Infrastructure for Remote Access to Microdata at Eurostat. Bachelor’s thesis., Vienna Univesity of Economics.10.1007/978-3-642-15838-4_22]Search in Google Scholar
[Reuter, W. and J-M. Museux 2010. “Establishing an Infrastructure for Remote Access to Microdata at Eurostat.” In Privacy in Statistical Databases: Lecture Notes in Computer Science, edited by J. Domingo-Ferrer. 249–257. New York: Springer.10.1007/978-3-642-15838-4_22]Search in Google Scholar
[Rinott, Y. 2003. “On Models for Statistical Disclosure Risk Estimation.” In Proceedings of the Joint ECE/Eurostat Work Session on Statistical Data Confidentiality. April 7–9, 2003. 275–285, United Nations Statistical Commission, Geneva.]Search in Google Scholar
[Shlomo, N. 2008. “Releasing Microdata: Disclosure Risk Estimation, Data Masking and Assessing Data Utility.” In Section on Survey Research Methods, JSM. August 3–7, 2008, Denver, Colorado, USA. 229–240. Available at: https://www.amstat.org/sections/srms/proceedings/y2008/Files/300242.pdf (accessed November 2015)]Search in Google Scholar
[Simón, H. 2010. “International Differences in Wage Inequality: A New Glance with European Matched Employer-Employee Data.” British Journal of Industrial Relations 48: 310–346.10.1111/j.1467-8543.2008.00708.x]Search in Google Scholar
[Stephan, G. and K. Gerlach. 2005. “Wage Settlements and Wage Settings: Evidence from a Multilevel Model.” Applied Economics 37: 2297–2306.10.1080/00036840500366429]Search in Google Scholar
[Stockinger, S. 2010. Frauenbericht 2010. Technical report, Federal Ministry for Women and the Civil Service of Austria. Vienna: Available at: http://www.bka.gv.at/site/6811/default.aspx (accessed December 2013)]Search in Google Scholar
[Sweeney, L. 2002. “k-Anonymity: a Model for Protecting Privacy.” International Journal on Uncertainty, Fuzziness and Knowledge-based Systems 10: 557–570.10.1142/S0218488502001648]Search in Google Scholar
[Templ, M. 2008. “Statistical Disclosure Control for Microdata Using the R-package sdcMicro.” Transactions on Data Privacy 1: 67–85.]Search in Google Scholar
[Templ, M. 2011a. Estimators and Model Predictions from the Structural Earnings Survey for Benchmarking Statistical Disclosure Methods. Research Report CS-2011-4, Department of Statistics and Probability Theory, Vienna University of Technology, Vienna, Austria.]Search in Google Scholar
[Templ, M. 2011b. “Comparison of Perturbation Methods Based on Pre-defined Quality Indicators.” In Joint UNECE/Eurostat work session on statistical data confidentiality, 26–28 October, 2011, Tarragona, Spain, 1–10. Unece, Geneva, Italy.]Search in Google Scholar
[Templ, M. and A. Alfons. 2011. Variance Estimation of Social Inclusion Indicators Using the R Package laeken. Research Report CS-2011-3, Department of Statistics and Probability Theory, Vienna University of Technology. Available at: http://www.statistik.tuwien.ac.at/forschung/CS/CS-2011-3complete.pdf (accessed December 2013)]Search in Google Scholar
[Templ, M. and P. Filzmoser. 2014. “Simulation and Quality of a Synthetic Close-to-Reality Employer-Employee Population.” Journal of Applied Statistics, 41: 1053–1072.10.1080/02664763.2013.859237]Search in Google Scholar
[Templ, M. and B. Meindl. 2010. “Practical Applications in Statistical Disclosure Control Using R.” In Privacy and Anonymity in Information Management Systems: Advanced Information and Knowledge Processing, edited by J. Nin and J. Herranz. 31–62. London: Springer.10.1007/978-1-84996-238-4_3]Search in Google Scholar
[Templ, M. A. Kowarik, and B. Meindl. 2015. “Statistical Disclosure Control for Micro-Data Using R Package sdcMicro.” Journal of Statistical Software. 67: 1–36.10.18637/jss.v067.i04]Search in Google Scholar
[Weinberg, D.H. 2007. “Earnings by Gender: Evidence from Census 2000.” Monthly Labor Review Online 130: 26–34.]Search in Google Scholar
[Winter-Ebmer, R. and J. Zweimüller. 1999. “Firm Size Wage Differentials in Switzerland: Evidence from Job Changers.” American Economic Review 89: 89–93.10.1257/aer.89.2.89]Search in Google Scholar
[Woo, M., J.P. Reiter, A. Oganian, and A.F. Karr. 2009. “Global Measures of Data Utility for Microdata Masked for Disclosure Limitation.” Journal of Privacy and Confidentiality 1: 111–124.10.29012/jpc.v1i1.568]Search in Google Scholar