IDEAS home Printed from https://ideas.repec.org/p/kof/wpskof/14-363.html
   My bibliography  Save this paper

Unveiling Participant Level Determinants of Unit Non-Response in Business Tendency Surveys

Author

Abstract

Business Tendency Surveys (BTS) continue to be an important source of timely informa- tion on business cycles in many countries. We address quality of economic survey data by uncovering the relation between unit non-response and participant characteristics on company respectively respondent level. We use a unique, matched dataset that merges rich business tendency survey panel data with data from an exclusively conducted meta survey. Our meta information enhances the set of rm characteristics by information such as valuation of business tendency surveys or perceived response burden. We use dierent count data models to explain non-response count. Our models include weighted count data regressions as well as a two part hurdle model. We nd that response burden, a company's survey track record, timeliness and participation mode are the strongest and most robust predictors of unit non-response. We also nd a weaker negative eect of the business situation on unit response. Remarkably we do not nd a signicant in uence of neither company size nor valuation of BTS on the propensity to respond to periodical qualitative BTS.

Suggested Citation

  • Matthias Bannert & Andreas Dibiasi, 2014. "Unveiling Participant Level Determinants of Unit Non-Response in Business Tendency Surveys," KOF Working papers 14-363, KOF Swiss Economic Institute, ETH Zurich.
    • Handle: RePEc:kof:wpskof:14-363
    DOI: 10.3929/ethz-a-010193021
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.3929/ethz-a-010193021
    Download Restriction: no
    File URL: https://libkey.io/10.3929/ethz-a-010193021?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Zeileis, Achim & Kleiber, Christian & Jackman, Simon, 2008. "Regression Models for Count Data in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 27(i08).
    2. Klaus Abberger & Matthias Bannert & Andreas Dibiasi, 2014. "Metaumfrage im Dienstleistungssektor," KOF Analysen, KOF Swiss Economic Institute, ETH Zurich, vol. 8(2), pages 51-62, June.
    3. 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.
    4. Zeileis, Achim, 2004. "Econometric Computing with HC and HAC Covariance Matrix Estimators," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 11(i10).
    5. Zeileis, Achim, 2006. "Object-oriented Computation of Sandwich Estimators," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 16(i09).
    6. Klaus Abberger & Manuel Birnbrich & Christian Seiler, 2009. "Survey of the survey in distribution - a meta-survey on the Ifo Business Survey," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 62(21), pages 34-41, November.
    7. Klaus Abberger & Stefan Sauer & Christian Seiler, 2011. "Der Test des Tests im ifo Konjunkturtest Handel," ifo Forschungsberichte, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, number 52.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Andreas Dibiasi & David Iselin, 2021. "Measuring Knightian uncertainty," Empirical Economics, Springer, vol. 61(4), pages 2113-2141, October.
    2. Binding, Garret & Dibiasi, Andreas, 2017. "Exchange rate uncertainty and firm investment plans evidence from Swiss survey data," Journal of Macroeconomics, Elsevier, vol. 51(C), pages 1-27.

    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. Zeileis, Achim & Koenker, Roger, 2008. "Econometrics in R: Past, Present, and Future," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 27(i01).
    2. Sarah Harris & Wendy Anderson & Musa Kilinc & Liam Fogarty, 2012. "The relationship between fire behaviour measures and community loss: an exploratory analysis for developing a bushfire severity scale," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 63(2), pages 391-415, September.
    3. James R. Hines & Niklas Potrafke & Marina Riem & Christoph Schinke, 2019. "Inter vivos transfers of ownership in family firms," International Tax and Public Finance, Springer;International Institute of Public Finance, vol. 26(2), pages 225-256, April.
    4. Stefan Seifert & Christoph Kahle & Silke Hüttel, 2021. "Price Dispersion in Farmland Markets: What Is the Role of Asymmetric Information?," American Journal of Agricultural Economics, John Wiley & Sons, vol. 103(4), pages 1545-1568, August.
    5. Sviták, Jan & Tichem, Jan & Haasbeek, Stefan, 2021. "Price effects of search advertising restrictions," International Journal of Industrial Organization, Elsevier, vol. 77(C).
    6. Timo Dimitriadis & Xiaochun Liu & Julie Schnaitmann, 2020. "Encompassing Tests for Value at Risk and Expected Shortfall Multi-Step Forecasts based on Inference on the Boundary," Papers 2009.07341, arXiv.org.
    7. Christopher F. Parmeter, 2018. "Estimation of the two-tiered stochastic frontier model with the scaling property," Journal of Productivity Analysis, Springer, vol. 49(1), pages 37-47, February.
    8. Hasler Mario, 2013. "Multiple Contrasts for Repeated Measures," The International Journal of Biostatistics, De Gruyter, vol. 9(1), pages 1-13, July.
    9. Zeileis, Achim & Kleiber, Christian & Jackman, Simon, 2008. "Regression Models for Count Data in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 27(i08).
    10. Mario Hasler, 2016. "Heteroscedasticity: multiple degrees of freedom vs. sandwich estimation," Statistical Papers, Springer, vol. 57(1), pages 55-68, March.
    11. Kleiber Christian & Zeileis Achim, 2010. "The Grunfeld Data at 50," German Economic Review, De Gruyter, vol. 11(4), pages 404-417, December.
    12. Lupi, Claudio, 2009. "Unit Root CADF Testing with R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 32(i02).
    13. Wilms, Rafael & Dahan, Clara Seif el, 2022. "Are we on the same page? The moderating role of value congruence in charismatic signaling-charismatic effects relationship," OSF Preprints s5q6d, Center for Open Science.
    14. Andersson, Jonas & Schroyen, Fred & Torsvik, Gaute, 2019. "The impact of international tax information exchange agreements on the use of tax amnesty: evidence from Norway," Discussion Papers 2019/12, Norwegian School of Economics, Department of Business and Management Science.
    15. Christian Kleiber & Achim Zeileis, 2010. "The Grunfeld Data at 50," German Economic Review, Verein für Socialpolitik, vol. 11(4), pages 404-417, November.
    16. Beth Ann Griffin & Megan S. Schuler & Elizabeth A. Stuart & Stephen Patrick & Elizabeth McNeer & Rosanna Smart & David Powell & Bradley Stein & Terry Schell & Rosalie Liccardo Pacula, 2020. "Variation in Performance of Commonly Used Statistical Methods for Estimating Effectiveness of State-Level Opioid Policies on Opioid-Related Mortality," NBER Working Papers 27029, National Bureau of Economic Research, Inc.
    17. Ghislain B. D. Aïhounton & Arne Henningsen & Neda Trifkovic, 2021. "Pesticide Handling and Human Health: Conventional and Organic Cotton Farming in Benin," IFRO Working Paper 2021/06, University of Copenhagen, Department of Food and Resource Economics.
    18. Sakari Lähdemäki, 2017. "Traditional convergence tests with Penn World Table 9.0," Working Papers 309, Työn ja talouden tutkimus LABORE, The Labour Institute for Economic Research LABORE.
    19. W. Lee, 2014. "Historical global analysis of occurrences and human casualty of extreme temperature events (ETEs)," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 70(2), pages 1453-1505, January.
    20. Binding, Garret & Dibiasi, Andreas, 2017. "Exchange rate uncertainty and firm investment plans evidence from Swiss survey data," Journal of Macroeconomics, Elsevier, vol. 51(C), pages 1-27.

    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:kof:wpskof:14-363. 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: the person in charge (email available below). General contact details of provider: https://edirc.repec.org/data/koethch.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.