Abstract
This paper uses a natural experiment—the Super Bowl—to study the causal effect of advertising on demand for movies. Identification of the causal effect rests on two points: 1) Super Bowl ads are purchased before advertisers know which teams will play; 2) home cities of the teams that are playing will have proportionally more viewers than viewers in other cities. We find that the movies in our sample experience on average incremental opening weekend ticket sales of about $8.4 million from a $3 million Super Bowl advertisement.
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Notes
We also include city and movie fixed effects along with an index of Google searches prior to the Super Bowl as control variables in our regressions.
The number of queries in a given city must be larger than an unspecified privacy threshold to show up in the index, so there are a few smaller cities that report zero searches on movie entities prior to the Super Bowl. We drop these cities from the analysis.
These are available at http://www.vegasinsider.com/nfl/afc-championship/history/.
Another question is whether placebo movies do worse than they would have if the Super Bowl ads had not run. That is, does advertising for Super Bowl movies cause substitution away from placebo movies? The relevant coefficient to test this is the first one in Table 8, Nielsen Super Bowl Ratings. Unfortunately, we get different answers depending on the specification. It is usually negative – suggesting there is substitution – but only statistically significant in one out of four main specifications.
Hartmann and Klapper (2017) estimate a 153 percent ROI for Super Bowl beer ads, but caution that this is a likely an overestimate.
Inferred age and gender are based on web site visits and inferred income is based on the IP address of the respondent and Census data.
In general, we don’t have sufficient power to break down the treatment effects. There are several other interesting questions, such as whether there are differential effects for movies with more competition, but we have to leave these questions for further research. It may be possible to investigate such issues after we accumulate a few more years of Super Bowl data.
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This paper benefited greatly from discussions with Randall Lewis, David Reiley, Bo Cowgill, Lawrence Katz, and Lawrence Summers. The referees and editors were particularly helpful. We also thank participants at IO Fest at Berkeley and the NBER Summer Institute for helpful comments.
Appendices
Appendix A: Press Reports About Super Bowl Ad Sales
Season | Report Date | Status |
|---|---|---|
(Days Before Game) | ||
2002 | January 6, 2003 (23) | “NBC says it has fewer than 10 spots available” 1 |
2003 | N/A | N/A |
2004 | February 3, 2005 (3) | “Fox said Thursday that all 59 slots had been sold.” 2 |
2005 | December 18, 2005 (49) | 80 % sold 3 |
2006 | January 3, 2007 (32) | first half sold out 4 |
2007 | November 7, 2007 (88) | 90 % sold out 5 |
2008 | October 1, 2008 (123) | most of the slots were sold out by September 6 |
2009 | February 1, 2010 (6) | CBS executives said they had finished selling commercial time 7 |
2010 | October 29, 2010 (100) | “Advertising inventory in next year’s Super Bowl has sold out” 8 |
2011 | January 2, 2012 (34) | sold out 9 |
2012 | September 3, 2012 (153) | “Super Bowl advertisers commit super early” 10 |
Footnote 8 Footnote 9 Footnote 10 Footnote 11 Footnote 12 Footnote 13 Footnote 14 Footnote 15 Footnote 16 Footnote 17 Footnote 18
Appendix B: List of advertised and placebo movies
Appendix C: Coefficients for our other specifications
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Stephens-Davidowitz, S., Varian, H. & Smith, M.D. Super returns to Super Bowl ads?. Quant Mark Econ 15, 1–28 (2017). https://doi.org/10.1007/s11129-016-9179-0
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DOI: https://doi.org/10.1007/s11129-016-9179-0