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Occupation coding during the interview

Author

Listed:
  • Schierholz, Malte

    (Institute for Employment Research (IAB), Nuremberg, Germany)

  • Gensicke, Miriam

    (TNS Infratest Sozialforschung)

  • Tschersich, Nikolai

    (TNS Infratest Sozialforschung)

Abstract

"Currently, most surveys ask for occupation with open-ended questions. The verbatim responses are coded afterwards, which is error-prone and expensive. We describe an alternative approach that allows occupation coding during the interview. Our new technique utilizes a supervised learning algorithm to predict candidate job categories. These suggestions are presented to the respondent, who can in turn choose the most adequate occupation. 72.4% of the respondents selected an occupation when the new instrument was tested in a telephone survey, implicating potential cost savings. To aid further improvements, we identify a number of factors how to increase quality and reduce interview duration." (Author's abstract, IAB-Doku) ((en))

Suggested Citation

  • Schierholz, Malte & Gensicke, Miriam & Tschersich, Nikolai, 2016. "Occupation coding during the interview," IAB-Discussion Paper 201617, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].
  • Handle: RePEc:iab:iabdpa:201617
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    File URL: https://doku.iab.de/discussionpapers/2016/dp1716.pdf
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    References listed on IDEAS

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    1. repec:iab:iabfme:201204(de is not listed on IDEAS
    2. Vom Berge, Philipp & König, Marion & Seth, Stefan, 2013. "Sample of Integrated Labour Market Biographies (SIAB) 1975-2010," FDZ Datenreport. Documentation on Labour Market Data 201301_en, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].
    3. Mario Bossler & Hans-Dieter Gerner, 2020. "Employment Effects of the New German Minimum Wage: Evidence from Establishment-Level Microdata," ILR Review, Cornell University, ILR School, vol. 73(5), pages 1070-1094, October.
    4. Tijdens Kea, 2014. "Dropout Rates and Response Times of an Occupation Search Tree in a Web Survey," Journal of Official Statistics, Sciendo, vol. 30(1), pages 23-43, March.
    5. Trappmann, Mark & Beste, Jonas & Bethmann, Arne & Müller, Gerrit, 2013. "The PASS panel survey after six waves," Journal for Labour Market Research, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany], vol. 46(4), pages 275-281.
    6. Mario Bossler, 2017. "Employment expectations and uncertainties ahead of the new German minimum wage," Scottish Journal of Political Economy, Scottish Economic Society, vol. 64(4), pages 327-348, September.
    7. Drasch, Katrin & Matthes, Britta & Munz, Manuel & Paulus, Wiebke & Valentin, Margot-Anna, 2012. "Arbeiten und Lernen im Wandel : Teil V: Die Codierung der offenen Angaben zur beruflichen Tätigkeit, Ausbildung und Branche," FDZ Methodenreport 201204_de, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].
    8. Feinerer, Ingo & Hornik, Kurt & Meyer, David, 2008. "Text Mining Infrastructure in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 25(i05).
    9. Mark Trappmann & Jonas Beste & Arne Bethmann & Gerrit Müller, 2013. "The PASS panel survey after six waves [Die PASS-Panelbefragung nach sechs Wellen]," Journal for Labour Market Research, Springer;Institute for Employment Research/ Institut für Arbeitsmarkt- und Berufsforschung (IAB), vol. 46(4), pages 275-281, December.
    10. Schierholz, Malte, 2014. "Automating survey coding for occupation," FDZ Methodenreport 201410_en, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].
    11. repec:iab:iabfme:201410(en is not listed on IDEAS
    12. Conrad Frederick G. & Couper Mick P. & Sakshaug Joseph W., 2016. "Classifying Open-Ended Reports: Factors Affecting the Reliability of Occupation Codes," Journal of Official Statistics, Sciendo, vol. 32(1), pages 75-92, March.
    13. Peter Elias, 1997. "Occupational Classification (ISCO-88): Concepts, Methods, Reliability, Validity and Cross-National Comparability," OECD Labour Market and Social Policy Occasional Papers 20, OECD Publishing.
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    More about this item

    Keywords

    Beruf ; Codierung ; Datenqualität ; Interview;
    All these keywords.

    JEL classification:

    • J40 - Labor and Demographic Economics - - Particular Labor Markets - - - General
    • C83 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Survey Methods; Sampling Methods

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