Publications on the topic: Research Data and Methods - IAB - Institut für Arbeitsmarkt- und Berufsforschung

Type of publication

Year of publication

Total hits 1.017

IAB-Stellenerhebung: Betriebsbefragung zu Stellenangebot und Besetzungsprozessen, Welle 2000 bis 2021 mit Folgequartalen von 2006 bis 2022

Börschlein, E., Diegmann, A., Gürtzgen, N., Kubis, A., Pirralha, A., Pohlan, L., Popp, M. & Vetter, F. (2024): IAB-Stellenerhebung: Betriebsbefragung zu Stellenangebot und Besetzungsprozessen, Welle 2000 bis 2021 mit Folgequartalen von 2006 bis 2022. (FDZ-Datenreport 06/2024 (de)), Nürnberg, 27 p. DOI:10.5164/IAB.FDZD.2406.de.v1
PASS-Befragungsdaten verknüpft mit administrativen Daten des IAB (PASS-ADIAB) 1975-2022

Dummert, S. & Sauer, I. (2024): PASS-Befragungsdaten verknüpft mit administrativen Daten des IAB (PASS-ADIAB) 1975-2022. (FDZ-Datenreport 05/2024), Nürnberg, 82 p. DOI:10.5164/IAB.FDZD.2405.de.v1
Bridging Between Different BeH Industry Classifications via Imputation

Drechsler, J. & Ludsteck, J. (2024): Bridging Between Different BeH Industry Classifications via Imputation. (FDZ-Methodenreport 04/2024 (en)), Nürnberg, 17 p. DOI:10.5164/IAB.FDZM.2404.en.v1
Generating Synthetic Data is Complicated: Know Your Data and Know Your Generator

Latner, J., Neunhoeffer, M. & Drechsler, J. (2024): Generating Synthetic Data is Complicated: Know Your Data and Know Your Generator. In: J. Domingo-Ferrer & M. Önen (Hrsg.) (2024): Privacy in Statistical Databases 2024, p. 115-128. DOI:10.1007/978-3-031-69651-0_8
Evaluating the Pseudo Likelihood Approach for Synthesizing Surveys Under Informative Sampling

Oganian, A., Drechsler, J. & Iqbal, M. (2024): Evaluating the Pseudo Likelihood Approach for Synthesizing Surveys Under Informative Sampling. In: J. Domingo-Ferrer & M. Önen (Hrsg.) (2024): Privacy in Statistical Databases 2024, p. 129-143. DOI:10.1007/978-3-031-69651-0_9
An Evaluation of Synthetic Data Generators Implemented in the Python Library Synthcity

Fössing, E. & Drechsler, J. (2024): An Evaluation of Synthetic Data Generators Implemented in the Python Library Synthcity. In: J. Domingo-Ferrer & M. Önen (Hrsg.) (2024): Privacy in Statistical Databases 2024, p. 178-193. DOI:10.1007/978-3-031-69651-0_12
The Complexities of Differential Privacy for Survey Data

Drechsler, J. & Bailie, J. (2024): The Complexities of Differential Privacy for Survey Data. (NBER working paper / National Bureau of Economic Research 32905), Cambridge, Mass, 18 p.
The Impact of Mail, Web, and Mixed-Mode Data Collection on Participation in Establishment Surveys

Küfner, B., Sakshaug, J., Zins, S. & Globisch, C. (2025): The Impact of Mail, Web, and Mixed-Mode Data Collection on Participation in Establishment Surveys. In: Journal of survey statistics and methodology, Vol. 13, No. 1, p. 66-99. DOI:10.1093/jssam/smae033
Enriching administrative data using survey data and machine learning techniques

Kunaschk, M. (2024): Enriching administrative data using survey data and machine learning techniques. In: Economics Letters, Vol. 243. DOI:10.1016/j.econlet.2024.111924
Linked-Employer-Employee-Daten des IAB: LIAB-Längsschnittmodell (LIAB LM) 1975-2021

Panahian Fard, D., Schmucker, A., Seth, S., Umkehrer, M. & Zimmermann, F. (2024): Linked-Employer-Employee-Daten des IAB: LIAB-Längsschnittmodell (LIAB LM) 1975-2021. (FDZ-Datenreport 04/2024 (de)), Nürnberg, 76 p. DOI:10.5164/IAB.FDZD.2404.de.v1
Linked-Employer-Employee-Data of the IAB: LIAB Longitudinal Model (LIAB LM) 1975-2021

Panahian Fard, D., Schmucker, A., Seth, S., Umkehrer, M. & Zimmermann, F. (2024): Linked-Employer-Employee-Data of the IAB: LIAB Longitudinal Model (LIAB LM) 1975-2021. (FDZ-Datenreport 04/2024 (en)), Nürnberg, 76 p. DOI:10.5164/IAB.FDZD.2404.en.v1
Collecting Hair Samples in Online Panel Surveys: Participation Rates, Selective Participation, and Effects on Attrition

Lawes, M., Hetschko, C., Sakshaug, J. & Eid, M. (2024): Collecting Hair Samples in Online Panel Surveys: Participation Rates, Selective Participation, and Effects on Attrition. In: Survey research methods, Vol. 18, No. 2, p. 167-185. DOI:10.18148/srm/2024.v18i2.8170
Linking the Mannheim Enterprise Panel (MUP) with Administrative Establishment Data of IAB

Diegmann, A., Doherr, T., Hälbig, M. & Wolter, S. (2024): Linking the Mannheim Enterprise Panel (MUP) with Administrative Establishment Data of IAB. (FDZ-Methodenreport 03/2024), Nürnberg, 20 p. DOI:10.5164/IAB.FDZM.2403.en.v1
Mannheimer Unternehmenspanel verknüpft mit dem Betriebs-Historik-Panel 2010–2020 (MUP-BHP 1020)

Diegmann, A., Gottschalk, S., Hälbig, M., Schmucker, A. & Wolter, S. (2024): Mannheimer Unternehmenspanel verknüpft mit dem Betriebs-Historik-Panel 2010–2020 (MUP-BHP 1020). (FDZ-Datenreport 03/2024 (de)), Nürnberg, 106 p. DOI:10.5164/IAB.FDZD.2403.de.v1
The Mannheim Enterprise Panel linked to the Establishment History Panel of the IAB 2010–2020 (MUP-BHP 1020)

Diegmann, A., Gottschalk, S., Hälbig, M., Schmucker, A. & Wolter, S. (2024): The Mannheim Enterprise Panel linked to the Establishment History Panel of the IAB 2010–2020 (MUP-BHP 1020). (FDZ-Datenreport 03/2024 (en)), Nürnberg, 104 p. DOI:10.5164/IAB.FDZD.2403.en.v1
NEPS-SC3-Erhebungsdaten verknüpft mit administrativen Daten des IAB (NEPS-SC3-ADIAB)

Bachbauer, N. (2024): NEPS-SC3-Erhebungsdaten verknüpft mit administrativen Daten des IAB (NEPS-SC3-ADIAB). (FDZ-Datenreport 02/2024 (de)), Nürnberg, 90 p. DOI:10.5164/IAB.FDZD.2402.de.v1
NEPS-SC3 survey data linked to administrative data of the IAB (NEPS-SC3-ADIAB)

Bachbauer, N. (2024): NEPS-SC3 survey data linked to administrative data of the IAB (NEPS-SC3-ADIAB). (FDZ-Datenreport 02/2024 (en)), Nürnberg, 88 p. DOI:10.5164/IAB.FDZD.2402.en.v1
Measurement error in longitudinal earnings data: evidence from Germany

Schmillen, A., Umkehrer, M. & Wachter, T. (2024): Measurement error in longitudinal earnings data: evidence from Germany. In: Journal for labour market research, Vol. 58. DOI:10.1186/s12651-024-00366-x
Continual Release of Differentially Private Synthetic Data from Longitudinal Data Collections

Bun, M., Gaboardi, M., Neunhoeffer, M. & Zhang, W. (2024): Continual Release of Differentially Private Synthetic Data from Longitudinal Data Collections. In: Proceedings of the ACM on Management of Data, Vol. 2, No. 2, p. 1-26. DOI:10.1145/3651595
Whose Data Is It Anyway? Towards a Formal Treatment of Differential Privacy for Surveys

Bailie, J. & Drechsler, J. (2024): Whose Data Is It Anyway? Towards a Formal Treatment of Differential Privacy for Surveys. In: National Bureau of Economic Research (2024): Data Privacy Protection and the Conduct of Applied Research: Methods, Approaches and their Consequences, Spring 2024, Washington, p. 1-33.