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The determinants of unit non-response in the Ifo Business Survey

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  • Christian Seiler

Abstract

It is well-known that non-response affects the results of surveys and can even cause biases due to selectivities if it cannot be regarded as missing at random. In contrast to household surveys, response behaviour in business surveys is rarely examined in literature. This paper analyses a large business survey at a microdata level for unit non-response. The data base is the Ifo Business Survey, which was established in 1949 and is completed by more than 5000 firms every month. The panel structure makes it possible to use statistical modelling with the inclusion of different types of time dimensions, as well as firm-specific effects. The results show that there are strong time-dependent effects on the response rate and that non-response is more frequent in economic good times. Copyright Springer-Verlag Berlin Heidelberg 2014

Suggested Citation

  • Christian Seiler, 2014. "The determinants of unit non-response in the Ifo Business Survey," AStA Wirtschafts- und Sozialstatistisches Archiv, Springer;Deutsche Statistische Gesellschaft - German Statistical Society, vol. 8(3), pages 161-177, September.
    • Handle: RePEc:spr:astaws:v:8:y:2014:i:3:p:161-177
    DOI: 10.1007/s11943-014-0142-9
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    References listed on IDEAS

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    1. Christian Seiler, 2012. "The Data Sets of the LMU-ifo Economics & Business Data Center – A Guide for Researchers," Schmollers Jahrbuch : Journal of Applied Social Science Studies / Zeitschrift für Wirtschafts- und Sozialwissenschaften, Duncker & Humblot, Berlin, vol. 132(4), pages 609-618.
    2. Kholodilin Konstantin Arkadievich & Siliverstovs Boriss, 2006. "On the Forecasting Properties of the Alternative Leading Indicators for the German GDP: Recent Evidence," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 226(3), pages 234-259, June.
    3. Klaus Abberger & Klaus Wohlrabe, 2006. "Einige Prognoseeigenschaften des ifo Geschäftsklimas - Ein Überblick über die neuere wissenschaftliche Literatur," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 59(22), pages 19-26, November.
    4. Horst Rottmann & Timo Wollmershäuser, 2013. "A micro data approach to the identification of credit crunches," Applied Economics, Taylor & Francis Journals, vol. 45(17), pages 2423-2441, June.
    5. Drechsel, Katja & Scheufele, Rolf, 2012. "The performance of short-term forecasts of the German economy before and during the 2008/2009 recession," International Journal of Forecasting, Elsevier, vol. 28(2), pages 428-445.
    6. Nikolay Robinzonov & Klaus Wohlrabe, 2010. "Freedom of Choice in Macroeconomic Forecasting ," CESifo Economic Studies, CESifo Group, vol. 56(2), pages 192-220, June.
    7. Pesaran, M. Hashem & Timmermann, Allan, 2009. "Testing Dependence Among Serially Correlated Multicategory Variables," Journal of the American Statistical Association, American Statistical Association, vol. 104(485), pages 325-337.
    8. R?diger Bachmann & Steffen Elstner & Eric R. Sims, 2013. "Uncertainty and Economic Activity: Evidence from Business Survey Data," American Economic Journal: Macroeconomics, American Economic Association, vol. 5(2), pages 217-249, April.
    9. Klaus Abberger & Manuel Birnbrich & Christian Seiler, 2009. "Der »Test des Tests« im Handel – eine Metaumfrage zum ifo Konjunkturtest," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 62(21), pages 34-41, November.
    10. Florian Janik & Susanne Kohaut, 2012. "Why don’t they answer? Unit non-response in the IAB establishment panel," Quality & Quantity: International Journal of Methodology, Springer, vol. 46(3), pages 917-934, April.
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    Cited by:

    1. Ralf Thomas Münnich, 2014. "Vorwort des Herausgebers," AStA Wirtschafts- und Sozialstatistisches Archiv, Springer;Deutsche Statistische Gesellschaft - German Statistical Society, vol. 8(3), pages 89-90, September.
    2. Sarah Miller & David Amirault & Laurent Martin, 2017. "What’s Up with Unit Non-Response in the Bank of Canada’s Business Outlook Survey? The Effect of Staff Tenure," Discussion Papers 17-11, Bank of Canada.
    3. Magdolna Hiersemenzel & Stefan Sauer & Klaus Wohlrabe, 2022. "On the Representativeness of the ifo Business Survey," CESifo Working Paper Series 9863, CESifo.
    4. Fang, Tony & Xiao, Na & Zhu, Jane & Hartley, John, 2022. "Employer Attitudes and the Hiring of Immigrants and International Students: Evidence from a Survey of Employers in Canada," IZA Discussion Papers 15226, Institute of Labor Economics (IZA).
    5. Link Sebastian, 2020. "Harmonization of the ifo Business Survey’s Micro Data," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 240(4), pages 543-555, August.
    6. Benjamin Küfner & Joseph W. Sakshaug & Stefan Zins, 2022. "Establishment survey participation during the COVID-19 pandemic," Journal for Labour Market Research, Springer;Institute for Employment Research/ Institut für Arbeitsmarkt- und Berufsforschung (IAB), vol. 56(1), pages 1-18, December.
    7. Sebastian Link, 2018. "Harmonization and Interpretation of the ifo Business Survey's Micro Data," CESifo Working Paper Series 7427, CESifo.
    8. Christian Seiler, 2014. "Mode Preferences in Business Surveys: Evidence from Germany," ifo Working Paper Series 193, ifo Institute - Leibniz Institute for Economic Research at the University of Munich.

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    More about this item

    Keywords

    Business survey; Unit non-response; Generalised Estimation Equation; Panel survey; C33; C44; C81; C83;
    All these keywords.

    JEL classification:

    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • C44 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Operations Research; Statistical Decision Theory
    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access
    • C83 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Survey Methods; Sampling Methods

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