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

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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
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    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. "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.
    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, July.
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    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.

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