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Measuring non-response bias in a cross-country enterprise survey

Author

Listed:
  • Pérez-Duarte, Sébastien
  • Bańkowska, Katarzyna
  • Osiewicz, Małgorzata

Abstract

Non-response is a common issue affecting the vast majority of surveys, and low non-response is usually associated with higher quality. However, efforts to convince unwilling respondents to participate in a survey might not necessarily result in a better picture of the target population. It can lead to higher, rather than lower, non-response bias, for example if incentives are effective only for particular groups, e.g. in a business survey, if the incentives tend to attract mainly larger companies or enterprises encountering financial difficulties. We investigate the impact of non-response in the European Commission and European Central Bank Survey on the Access to Finance of Enterprises (SAFE), which collects evidence on the financing conditions faced by European small and medium-sized enterprises compared with those of large firms. This survey, which has been conducted by telephone biannually since 2009 by the European Central Bank and the European Commission, provides a valuable means of searching for this kind of bias, given the high heterogeneity of response propensities across countries. The study relies on so-called JEL Classification: C81, C83, D22

Suggested Citation

  • Pérez-Duarte, Sébastien & Bańkowska, Katarzyna & Osiewicz, Małgorzata, 2015. "Measuring non-response bias in a cross-country enterprise survey," Statistics Paper Series 12, European Central Bank.
    • Handle: RePEc:ecb:ecbsps:201512
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    References listed on IDEAS

    as
    1. Barry Schouten & Jelke Bethlehem & Koen Beullens & Øyvin Kleven & Geert Loosveldt & Annemieke Luiten & Katja Rutar & Natalie Shlomo & Chris Skinner, 2012. "Evaluating, Comparing, Monitoring, and Improving Representativeness of Survey Response Through R-Indicators and Partial R-Indicators," International Statistical Review, International Statistical Institute, vol. 80(3), pages 382-399, December.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    bias; business survey; non-response; R-indicators; representativity;
    All these keywords.

    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
    • D22 - Microeconomics - - Production and Organizations - - - Firm Behavior: Empirical Analysis

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