Measuring Position Effects in Search Advertising: A Regression Discontinuity Approach

We investigate the causal effect of position in search engine advertising listings on outcomes such as click-through rates and sales orders. Since positions are determined through an auction, there are significant selection issues in measuring position effects. Correlational results are likely to be biased due to the selection in position induced by strategic bidding by advertisers. Additionally, experimentation is rendered difficult in this situation by competitors’ bidding behavior, which induces selection biases that cannot be eliminated by randomizing the bids for the focal advertiser. We show that a regression discontinuity approach is a feasible approach to measure causal effects in this important context. We apply the approach to a unique dataset of 23.7 million daily observations containing information on a focal advertiser as well as its major competitors. Our regression discontinuity estimates show that causal position effects would be significantly underestimated if the selection of position is ignored. We find sharp local effects in the relationship between position and click through rates. Interestingly, we find that there are significant effects of position on sales orders only at relatively lower positions, with the top five positions not displaying position effects. We find that the effects vary across advertisers, a finding that has potential implications for theoretical work on position auctions. We also investigate differences in effects across weekdays and weekends, and across the broad and exact match targeting options offered by Google. An important finding is that while firms may be profitable in a short-term sense in their current positions, they could improve long-term profitability by moving up a position in the search advertising results.