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Correlates of record linkage and estimating risks of non‐linkage biases in business data sets

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  • Jamie C. Moore
  • Peter W. F. Smith
  • Gabriele B. Durrant

Abstract

Researchers often utilize data sets that link information from multiple sources, but non‐linkage biases caused by linked and non‐linked subject differences are little understood, especially in business data sets. We address these knowledge gaps by studying biases in linkable 2010 UK Small Business Survey data sets. We identify correlates of business linkage propensity, and also for the first time its components: consent to linkage and register identifier appendability. As well, we take a novel approach to evaluating non‐linkage bias risks, by computing data set representativeness indicators (comparable, decomposable sample subset similarity measures). We find that the main impacts on linkage propensities and bias risks are due to consenter–non‐consenter differences explicable given business survey response processes, and differences between subjects with and without identifiers caused by register undercoverage of very small businesses. We then discuss consequences for the analysis of linked business data sets, and implications of the evaluation methods we introduce for linked data set producers and users.

Suggested Citation

  • Jamie C. Moore & Peter W. F. Smith & Gabriele B. Durrant, 2018. "Correlates of record linkage and estimating risks of non‐linkage biases in business data sets," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 181(4), pages 1211-1230, October.
    • Handle: RePEc:bla:jorssa:v:181:y:2018:i:4:p:1211-1230
    DOI: 10.1111/rssa.12342
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    1. Melissa Bjelland & Bruce Fallick & John Haltiwanger & Erika McEntarfer, 2011. "Employer-to-Employer Flows in the United States: Estimates Using Linked Employer-Employee Data," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 29(4), pages 493-505, October.
    2. Schouten, Barry & Shlomo, Natalie & Skinner, Chris J., 2011. "Indicators for monitoring and improving representativeness of response," LSE Research Online Documents on Economics 39121, London School of Economics and Political Science, LSE Library.
    3. Felix Ritchie, 2008. "Secure access to confidential microdata: four years of the Virtual Microdata Laboratory," Economic & Labour Market Review, Palgrave Macmillan;Office for National Statistics, vol. 2(5), pages 29-34, May.
    4. Abowd, John M. & Vilhuber, Lars, 2005. "The Sensitivity of Economic Statistics to Coding Errors in Personal Identifiers," Journal of Business & Economic Statistics, American Statistical Association, vol. 23, pages 133-152, April.
    5. Robert Hayes & Catrin Omerod & Felix Ritchie, 2007. "Earnings: summary of sources and developments," Economic & Labour Market Review, Palgrave Macmillan;Office for National Statistics, vol. 1(1), pages 42-47, January.
    6. Alexander Hijzen & Richard Upward & Peter W. Wright, 2010. "Job Creation, Job Destruction and the Role of Small Firms: Firm‐Level Evidence for the UK," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 72(5), pages 621-647, October.
    7. 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.
    8. Barry Schouten & Natalie Shlomo, 2017. "Selecting Adaptive Survey Design Strata with Partial R-indicators," International Statistical Review, International Statistical Institute, vol. 85(1), pages 143-163, April.
    9. 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.
    10. repec:taf:jnlbes:v:30:y:2012:i:2:p:191-201 is not listed on IDEAS
    11. Timothy Dunne & J. Bradford Jensen & Mark J. Roberts, 2009. "Producer Dynamics: New Evidence from Micro Data," NBER Books, National Bureau of Economic Research, Inc, number dunn05-1, March.
    12. Shlomo, Natalie & Skinner, Chris J. & Schouten, Barry, 2012. "Estimation of an indicator of the representativeness of survey response," LSE Research Online Documents on Economics 39124, London School of Economics and Political Science, LSE Library.
    13. John J. Abowd & John Haltiwanger & Julia Lane, 2004. "Integrated Longitudinal Employer-Employee Data for the United States," American Economic Review, American Economic Association, vol. 94(2), pages 224-229, May.
    14. Timothy Dunne & J. Bradford Jensen & Mark J. Roberts, 2009. "Introduction to "Producer Dynamics: New Evidence from Micro Data"," NBER Chapters, in: Producer Dynamics: New Evidence from Micro Data, pages 1-12, National Bureau of Economic Research, Inc.
    15. Barry Schouten & Fannie Cobben & Peter Lundquist & James Wagner, 2016. "Does more balanced survey response imply less non-response bias?," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 179(3), pages 727-748, June.
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    Cited by:

    1. Valentin Reich, 2024. "Machine Learning Based Linkage of Company Data for Economic Research: Application to the EBDC Business Panels," ifo Working Paper Series 409, ifo Institute - Leibniz Institute for Economic Research at the University of Munich.
    2. Serena Pattaro & Nick Bailey & Chris Dibben, 2020. "Using Linked Longitudinal Administrative Data to Identify Social Disadvantage," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 147(3), pages 865-895, February.

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