IDEAS home Printed from https://ideas.repec.org/p/ces/ifowps/_193.html
   My bibliography  Save this paper

Mode Preferences in Business Surveys: Evidence from Germany

Author

Listed:
  • Christian Seiler

Abstract

With the world-wide spread of the internet in the 1990s, the conduction of web or e-mail surveys became popular in research. Although these surveys provide fast data collection and reduced costs, results may suffer from biases due to the survey mode. While a variety of studies concerning mode effects in household or individual surveys exists, only less is known in case of business surveys. To give a contribution to this topic, we analyse a large German business survey – the Ifo Business Survey – which is answered by about 7,000 respondents each month since 1949. In this paper, we focus on three different aspects with respect to the survey mode: data quality, respondents' profiles and response behavior. Our results show that e-mail or web surveys reduce nonresponse and are more likely used by larger firms which operate in technology-related business areas.

Suggested Citation

  • 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.
    • Handle: RePEc:ces:ifowps:_193
    as

    Download full text from publisher

    File URL: https://www.ifo.de/DocDL/IfoWorkingPaper-193.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Andrea Carriero & Massimiliano Marcellino, 2011. "Sectoral Survey‐based Confidence Indicators for Europe," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 73(2), pages 175-206, April.
    2. Seiler, Christian & Heumann, Christian, 2013. "Microdata imputations and macrodata implications: Evidence from the Ifo Business Survey," Economic Modelling, Elsevier, vol. 35(C), pages 722-733.
    3. 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.
    4. Michael Kleemann & Manuel Wiegand, 2014. "Are Real Effects of Credit Supply Overestimated? Bias from Firms' Current Situation and Future Expectations," ifo Working Paper Series 192, ifo Institute - Leibniz Institute for Economic Research at the University of Munich.
    5. 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.
    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. 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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Christian Seiler, 2015. "On the robustness of balance statistics with respect to nonresponse," OECD Journal: Journal of Business Cycle Measurement and Analysis, OECD Publishing, Centre for International Research on Economic Tendency Surveys, vol. 2014(2), pages 45-62.
    2. Christian Seiler, 2013. "Nonresponse in Business Tendency Surveys: Theoretical Discourse and Empirical Evidence," ifo Beiträge zur Wirtschaftsforschung, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, number 52.
    3. 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.
    4. Sebastian Link, 2018. "Harmonization and Interpretation of the ifo Business Survey's Micro Data," CESifo Working Paper Series 7427, CESifo.
    5. Robert Lehmann & Antje Weyh, 2016. "Forecasting Employment in Europe: Are Survey Results Helpful?," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 12(1), pages 81-117, September.
    6. 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.
    7. Wolfgang Nierhaus & Timo Wollmershäuser, 2016. "ifo Konjunkturumfragen und Konjunkturanalyse: Band II," ifo Forschungsberichte, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, number 72.
    8. 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.
    9. 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.
    10. Schlösser, Alexander, 2020. "Forecasting industrial production in Germany: The predictive power of leading indicators," Ruhr Economic Papers 838, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    11. Robert Lehmann, 2016. "Economic Growth and Business Cycle Forecasting at the Regional Level," ifo Beiträge zur Wirtschaftsforschung, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, number 65.
    12. Christian Seiler & Klaus Wohlrabe, 2013. "Das ifo Geschäftsklima und die deutsche Konjunktur," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 66(18), pages 17-21, October.
    13. Anna Sophia Ciesielski & Klaus Wohlrabe, 2011. "Sektorale Prognosen im Verarbeitenden Gewerbe," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 64(22), pages 27-35, November.
    14. Katja Rietzler & Sabine Stephan, 2012. "Monthly recession predictions in real time: A density forecast approach for German industrial production," IMK Working Paper 94-2012, IMK at the Hans Boeckler Foundation, Macroeconomic Policy Institute.
    15. Carstensen, Kai & Heinrich, Markus & Reif, Magnus & Wolters, Maik H., 2020. "Predicting ordinary and severe recessions with a three-state Markov-switching dynamic factor model," International Journal of Forecasting, Elsevier, vol. 36(3), pages 829-850.
    16. João C. Claudio & Katja Heinisch & Oliver Holtemöller, 2020. "Nowcasting East German GDP growth: a MIDAS approach," Empirical Economics, Springer, vol. 58(1), pages 29-54, January.
    17. Anne Musson & Damien Rousselière, 2020. "Exploring the effect of crisis on cooperatives: a Bayesian performance analysis of French craftsmen cooperatives," Applied Economics, Taylor & Francis Journals, vol. 52(25), pages 2657-2678, May.
    18. Magnus Reif, 2020. "Macroeconomics, Nonlinearities, and the Business Cycle," ifo Beiträge zur Wirtschaftsforschung, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, number 87.
    19. Robert Lehmann, 2021. "Forecasting exports across Europe: What are the superior survey indicators?," Empirical Economics, Springer, vol. 60(5), pages 2429-2453, May.
    20. Drechsel, Katja & Scheufele, Rolf, 2010. "Should We Trust in Leading Indicators? Evidence from the Recent Recession," IWH Discussion Papers 10/2010, Halle Institute for Economic Research (IWH).

    More about this item

    JEL classification:

    • 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

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ces:ifowps:_193. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Klaus Wohlrabe (email available below). General contact details of provider: https://edirc.repec.org/data/ifooode.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.