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Does Online Search Predict Sales? Evidence from Big Data for Car Markets in Germany and the UK

Author

Listed:
  • Georg von Graevenitz

    (Centre for Competition Policy, University of East Anglia)

  • Christian Helmers

    (Santa Clara University)

  • Valentine Millot

    (OECD)

  • Oliver Turnbull

    (Bristol University)

Abstract

We use online search data to predict car sales in the German and UK automobile industries. Search data subsume several distinct search motives, which are not separately observable. We develop a model linking search motives to observable search data and sales. The model shows that predictions of sales relying on observable search data as a proxy for pre-purchase search will be biased. We show how to remove the biases and estimate the effect of pre-purchase search on sales. To assist identification of this effect, we use the introduction of scrappage subsidies for cars in 2008/2009 as a quasi-natural experiment. We also show that online search data are (i) highly persistent over time, (ii) potentially subject to permanent shocks, and (iii) correlated across products, but to different extent. We address these challenges to estimation and inference by using recent econometric methods for large N, large T panels.

Suggested Citation

  • Georg von Graevenitz & Christian Helmers & Valentine Millot & Oliver Turnbull, 2016. "Does Online Search Predict Sales? Evidence from Big Data for Car Markets in Germany and the UK," Working Paper series, University of East Anglia, Centre for Competition Policy (CCP) 2016-07, Centre for Competition Policy, University of East Anglia, Norwich, UK..
    • Handle: RePEc:uea:ueaccp:2016_07
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    References listed on IDEAS

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