Super Bowl Babies": Do Counties with Super Bowl Winning Teams See Increases in Births Nine Months Later?

"Super Bowl Babies": Do Counties with Super Bowl Winning Teams see Increases in Births Nine Months Later?

George M. Hayward1 University of North Carolina at Chapel Hill

Anna Rybinska University of North Carolina at Chapel Hill

1Direct all correspondence to George M. Hayward, University of North Carolina at Chapel Hill, 155 Hamilton Hall - CB 3210, Chapel Hill, NC 27517. Email: [email protected]. Phone: 484.201.4864. The authors would like to thank Phil Morgan for helpful suggestions throughout the development of this paper. This research received support from the Population Research Training grant (T32 HD007168) and the Population Research Infrastructure Program (P2C HD050924) awarded to the Carolina Population Center at The University of North Carolina at Chapel Hill by the Eunice Kennedy Shriver National Institute of Child Health and Human Development. Super Bowl Babies

On February 7th, 2016, a Super Bowl commercial triggered a flurry of excitement across the Internet and social media platforms. Even before airing, it garnered online attention from the dozens of news outlets such as the NFL, Fox Sports, ESPN, Sports Illustrated, CBS, CNN, USA

Today, and the Huffington Post. The commercial begins with the following claim: "Data suggests 9 months after a Super Bowl victory, winning cities see a rise in births" (NFL 2016b).

The rest of the commercial features choirs of children – allegedly conceived on the nights of previous Super Bowls – singing a modified version of Seal's "Kiss from a Rose" while dressed in the regalia of their hometown football teams. The lyrics of the song tell a compelling, provocative, and plausible story:

"Ba-dada, da-da-da, da, da, da, Bada-da What makes the Super Bowl so Super? A day we adore It is a day So super, it's why we were born In the end When our team won Mom and Dad looked at each other One thing led to another that night (all) We're all babies! It all happened on the night that a Super Bowl was played Ooh, that's why we're here singing this sweet serenade And now Super Bowl 50 is here So much reason for cheer (Seal) Tonight." [The full version music video, available online, contains the additional lyrics,] "When there were no more jalapeno-chili-bacon chicken wings Mommy and dad They cuddled, canoodled, all night Baby! On that night our moms and dads we so filled with desire (Filled with desire!) And here we stand, a baby choir" (NFL 2016a).

The clear assumption here is that the euphoria following a Super Bowl victory leads to increased intercourse among local fans of that team. There is indeed some evidence that birth

1 Super Bowl Babies rates spike nine months after large sporting events (Montesinos et al. 2013). There is also some evidence, however, that perceived spikes in birth rates are not always supported by the data. This turned out to be the case with the New York City blackout, for example (Udry 1970).

Preliminary attempts to contact the National Football League (NFL®) have been unsuccessful, so it is impossible to exactly replicate or extend their analyses. Unfortunately, it is unclear where the NFL® obtained their data and how their analyses were conducted. It is also unclear how they define cities and whether their findings are within or between cities. It is quite possible that both winning cities and losing cities see increases in births, for example.

Given that the Super Bowl is an annual event in the United States that attracts millions of viewers and potential parents, it is worth investigating whether there are truly annual cohorts of

"Super Bowl Babies" that have heretofore gone unrecognized. To examine this phenomenon, we use county-level birth data across the United States for the nine Super Bowls that correspond to the 2003-2011 seasons. We test the NFL’s claim by first analyzing Super Bowl winning counties individually. We then compare Super Bowl winning counties to Super Bowl losing counties.

Given the NFL's assumption that the euphoria of victory leads to increases in intercourse that lead to births nine months later, counties of the winning teams should have larger increases in births than counties of losing teams. Finally, we test another idea that goes beyond the NFL commercial. While the Super Bowl is only a brief event on a single day, the NFL playoffs that precede it are a month long. Until teams are eliminated, there is a chance that they will eventually win the Super Bowl – a realization that likely produces some combination of hope, optimism, and positivity among the fans. With each playoff win, these feelings could become even more real, producing mini-euphoric moments at each advancement. In other words, winning the Super Bowl may produce the climax of euphoria, but each leg of the journey may be

2 Super Bowl Babies accompanied by gradually increasing euphoria. Therefore, following the NFL’s assumption that a euphoric sensation is linked to increased births, we also test whether a team’s tenure in the

NFL playoffs is associated with county-level birth increases.

METHODS

Data and Measures

Births. While the NFL commercial specifically claims that “cities” of winning teams see increases in births, we use county-level birth data here instead. Our first and primary reason for doing so is a practical one: it is publicly available. Second, there is ambiguity regarding the term

“city” and how it applies to NFL teams (for example, which cities correspond to the “Arizona”

Cardinals or “New England” Patriots?). Third, we believe that any true birth increases in cities will be absorbed and reflected by county data. This is for two reasons: 1) most counties under consideration include the cities of their teams and 2) it is likely that teams’ fans also reside in their respective counties. Following this second point, we do not presume that fans within proper city limits are necessarily more fanatical nor euphoric following a victory than fans a few miles away in the same county.

All county-level birth data are obtained from the CDC WONDER database (Centers for

Disease Control and Prevention 2016a, 2016b). Births are available by month for all counties in the United States from 2003-2014. However, counties containing less than 100,000 people are aggregated into an “unidentified” category, and we exclude these from analyses. This leaves 580 counties with populations over 100,000. Of these counties, 56 only have data for one year – 2014

– and this is because they were not populous enough to be included prior. Another 2 counties are missing data for only one year, which is also 2014, because they were no longer large enough to

3 Super Bowl Babies be included. These 58 counties are excluded from analyses, leaving 522 counties with monthly birth data for all 12 years.

Connecting counties to teams. Stadium names and locations are available on each NFL team’s website. The city listed for every stadium address is used to identify the corresponding county with Google Maps. Most teams (20 out of 32) play in a city that corresponds exactly to their team name (e.g., the Atlanta Falcons play in Atlanta), and thus the county of the stadium is the same as the county of the city. There are two special cases: the Baltimore Ravens and St.

Louis Rams. Baltimore and St. Louis are both cities and counties, and data are available for both.

For consistency, their counties are used instead of their cities. Of the remaining 12 teams, there are 5 whose names apply to broader regions. These are the Arizona Cardinals, Carolina Panthers,

Minnesota Vikings, New England Patriots, and Tennessee Titans. There are another 5 whose names actually belie their true location: the Dallas Cowboys (who play in Arlington, Texas), the

New York Giants and the New York Jets (who share a stadium and play in East Rutherford, New

Jersey), the San Francisco 49ers (who play in Santa Clara, California), and the Washington

Redskins (who play in Greater Landover, Maryland). Finally, there are 2 teams that play in a different city than their name suggests but within the same county as that city. These are the

Buffalo Bills (who play in Orchard Park, New York) and the Miami Dolphins (who play in

Miami Gardens, Florida). While different arguments can be made for which counties best represent the fans of these teams, all counties are chosen based upon the stadium’s location for consistency. Because the New York Giants and the New York Jets share a stadium, they also share a county and are thus conflated to represent one team. Fortunately, they only both make the playoffs on one occasion, and they exit after the exact same amount of time. Table 1 summarizes this information below.

4 Super Bowl Babies

[Table 1 about here.]

Super Bowls and playoffs. All playoff games, including Super Bowls, and their dates are obtained from NFL.com (NFL Enterprises 2016). We limit the analyses to seasons for which we have birth data at least 18 months before and after the October following each Super Bowl. This leaves us with nine Super Bowls and sets of playoffs that correspond to the 2003-2011 seasons, presented below in Table 2. Because seasons take place during the end of the calendar year, the playoffs and Super Bowl for a given season happen in the following calendar year. For example, the 2003 season ended on 12/28/2003. The first playoff games began on 1/3/04 and the Super

Bowl took place on 2/1/04. Therefore, the expected birth spike for the 2003 season is October

2004.

[Table 2 about here.]

Additional measures. Three dummy variables indicate whether counties have an NFL team (0-No, 1-Yes), have a team that competed in the Super Bowl (0-No, 1-Yes), and have a team that won the Super Bowl (0-No, 1-Yes). Every county also has an “exposure” to the playoffs, which is automatically zero for every county without an NFL team. Naturally, counties with NFL teams that did not make the playoffs also have an exposure of zero. For those counties with an NFL team that did make the playoffs, exposure is simply the number of days between the start of the playoffs and when their team lost. For this calculation, the final day of each season is considered the beginning of the playoffs; it is on this day that the playoff brackets are official and complete. Each year, 12 of the 32 NFL teams make the playoffs. The playoffs are always a single-elimination tournament until the Super Bowl, with playoff games being played on consecutive weekends. When only two teams remain (the Super Bowl contenders), one weekend is skipped, which allows for a two-week preparation period for each team. In all, the playoffs

5 Super Bowl Babies span five weeks, meaning that “exposure” to the playoffs ranges from 6 to 35 days for all teams that make them. This range means that majority of births resulting from exposure to the playoffs will actually be in September, not October. An illustration of this is presented below in Table 3.

As a result, when we test exposure effects, September will be our treatment month.

[Table 3 about here.]

In sum, of our 522 counties, 31 have an NFL team (we combine the New York Giants and the New York Jets) and 491 do not. For a given season, either 11 or 12 of the 31 counties with an NFL team will have some exposure to the playoffs while the rest will not (it will be 11 counties if both the Giants and the Jets make the playoffs). Two of these counties will be represented in the Super Bowl and one of them will experience a Super Bowl victory.

Analytical Strategy

In this paper, we test three separate effects: 1) the effect of winning a Super Bowl on the number of births in the winning team’s county, 2) the effect of participating in a Super Bowl on the number of births in each participant’s county, and 3) the effect of participating in the NFL playoffs on the number of births in each participant’s county. For these analyses, we follow three different identification strategies. However, in all three we look at one outcome: the number of births nine months after the night of Super Bowl.

We assume that the change in the birth trends resulting from the Super Bowl win will be most visible 35 to 39 weeks after the night of the Super Bowl. Most pregnancies in the United

States last between 35 to 39 weeks after conception (Jukic et al. 2013; Martin et al. 2015). We add two weeks to the conception date to approximate gestational age (Committee on Fetus and

Newborn 2004). As Table 2 shows, for all of the Super Bowls we are analyzing, the majority of babies conceived on the night of the game would be born in the following October. Accordingly,

6 Super Bowl Babies the cut-off point for the 2003 season will be defined as October 2004. For this reason, the treatment cut-off point is established in October and we will look at the discontinuity in the number of births – a “bump” in the number of births in that month which would signify an immediate effect of the Super Bowl night on births. We will also look at the change in the time trend following the cut-off month of October to identify any long-term effects of the Super Bowl night.

To identify the effect of winning a Super Bowl in the counties of winning teams, we compare the number of births in the October for the year when the team won the Super Bowl to the average monthly number of births in the county and then to the average number of births in the county in October. For this latter part, we use all nine October values from 2004 to 2012.

To compare the effects in the counties of the winning teams, we compare the birth trends in the counties of teams winning and losing in each Super Bowl using a comparative interrupted time series (CITS) design (Shadish, Cook, and Campbell 2002). We reconstruct the time trends in the number of births in counties of winning and losing teams and look for the discontinuity in the trend in the October of the year when both teams participated in the Super Bowl, as well as for the change in the trends in both counties after October.

The logic behind this approach is that both counties are exposed to the same event at the same time. Therefore, once we account for baseline differences between them and the individual time trends, we should observe similar changes in both counties nine months after the Super

Bowl win. If only the county of the winning team experiences a bump in the birth records in

October, or a change in the birth trend after October, this can be identified as a specific effect of winning the Super Bowl. Because each Super Bowl occurs at a different point in time (i.e., in a different year), the shock of Super Bowl may have differential effects across time. Accordingly,

7 Super Bowl Babies we compare the counties of winners and losers of each Super Bowl separately in nine regression equations:

푖ℎ 푖푖푖 푤푖푒 푖푖푖 = + ∗ 푖푚푒 + ∗ 푆퐵 + ∗ 푆퐵 + ∗ 푖푚푒 ∗ 푆퐵 + ∗ 푖푚푒

푤푖푒 푖푖푖 푤푖푒 푖푖푖 푤푖푒 where ∗ 푆퐵 + ∗ 푆퐵 ∗ 푆퐵 + ∗ 푖푚푒 ∗ 푆퐵 ∗ 푆퐵 +

 – number of births in the county each month

 time푖ℎ – 18 months before and after the treatment month of October (37 months total)

 SBparticipation – a dummy variable equal to 0 before the Super Bowl and 1 for all of the months including and after winning the Super Bowl for both teams participating

 SBwinner – a dummy variable equal to 0 for counties of losing teams and 1 for counties of winning teams

Under this specification, represents the effect of the Super Bowl win for the county of the winning team compared to the county of the losing team, and represents the change of the trend in the number of births nine months after the Super Bowl for the county of the winning team compared to the county of the losing team.

To identify the effect of exposure to the NFL playoffs, we look at trends in counties of each team that participated in the playoffs each season. There are twelve playoff teams every season, except in 2006 when there are eleven (both the New York Giants and New York Jets made it that year, and they are combined into one). We account for the variation in exposure with the following equation, and we now use September as the treatment month:

푖ℎ 푖푖푖 푖푖푖 = + ∗ 푖푚푒 + ∗ 푆퐵 + ∗ 푒푒 + ∗ 푖푚푒 ∗ 푆퐵 + ∗ 푖푚푒

푖푖푖 푖푖푖 where ∗ 푒푒 + ∗ 푒푒 ∗ 푆퐵 + ∗ 푖푚푒 ∗ 푒푒 ∗ 푆퐵 +

 – number of births in the county each month

 time푖ℎ – 18 months before and after the treatment month of September (37 months total)

8 Super Bowl Babies

 SBparticipation – a dummy variable equal to 0 before the Super Bowl and 1 for all of the months including and after winning the Super Bowl for both teams participating  exposure – a continuous variable ranging from 6 to 35 days.

We model the spike in births in September controlling for possible differences in the effect based on the exposure ( in the estimating equation) and for the trends in number of births after September ( in the estimating equation). We also estimated these models with

October as the treatment month and the results are the same.

PRELIMINARY RESULTS

Table 4 represents the number of births in the counties of teams that won the 2003 – 2011

Super Bowls: the monthly average for all 37 months, the average for all nine Octobers between

2003 – 2011, and the number of births in October following the Super Bowl win (for instance, for the Colts, we present the monthly average for the 37 months between April 2005 and April

2008, the average for all Octobers between 2003-2011, and the number of births in October

2006).

[Table 4 about here.]

The number of births in October of the Super Bowl year in the county of the winning team is usually slightly higher than the monthly average of the number of births – in five out of nine analyzed seasons, we see a spike in the number of births in October compared to the monthly average. For four seasons we see a decrease. However, the spikes and drops in Octobers are not big – only in one season, 2005, does the drop falls outside of the one standard deviation from the average. A similar pattern is observed when we look at the comparison between the

October average and the October of the Super Bowl year. In six instances out of nine, we observe a spike in the number of births for October. The highest value is observed for the 2006

Super Bowl – 89 more births were recorded in the county in October 2006 compared to the

9 Super Bowl Babies average number for all Octobers between 2003 and 2011. In four instances, the number for

October of the year of Super Bowl falls outside of the one standard deviation from the mean of all Octobers, and in one case it is not a spike but a drop in births.

Table 5 presents the comparisons of Super Bowl winners and losers in terms of birth spikes and changes in the births trend after corresponding months of October. A linear time trend provided the best model fit of the ones tested (linear, square and cubic).

[Table 5 about here.]

There are no statistically significant differences in terms of the spikes/drops in the number of births between the counties of the winning and losing teams. In five out of nine instances, the counties of winning teams record a higher number of births than the counties of the losing teams (controlling for the baseline differences), and in four out of nine instances the counties of winning teams record a lower number of births than the counties of losing teams. It is noteworthy that the differences in some years are large; for instance, the difference for 2006 is almost 1,400 births. The differences might not be statistically significant due to the small sample size (two counties and 74 months of observation in each regression). We compared the estimated

“spike” (or drop) in the number of births in October of the Super Bowl with the average monthly number of births for the 37 months of observation. Table 6 includes the estimated spike from the

CITS (the sum of and from Equation 1).

[Table 6 about here.]

The comparison of the estimates of the effect of the Super Bowl win on the births in the counties of winning teams with the average number of births in the counties are inconclusive.

The estimates represent up to a 44% increase in the average number of births, but they vary drastically in size and direction. In six out of nine instances, the effect of the Super Bowl on the

10 Super Bowl Babies number of births nine months later was negative. These estimates also contradict the results shown in Table 4, where the observed numbers of births in Octobers of the Super Bowl are larger than the average for the specific county. The results are possibly sensitive to expanding the range of time analyzed – we tested models that had 61 months (30 months before and after the

October) and observed differences between the estimates of the spike/drop as large as 1,926 births for the 2008 season. Yet, none of the results for models with 37 or 61 months of observation were statistically significant.

As our last step, examined whether the effects of the Super Bowl vary by exposure to the playoffs. Perhaps the longer the team is in the playoffs, the more involved the fans get. Table 7 represents the differences between the effects of the Super Bowl and the change in the time trend after the Super Bowl. We observe no consistent difference in the effect of the Super Bowl by exposure on the number of births in September and in the time trend after September. As with the two previous effects, the estimates range in direction from positive to negative and most exposure effects are small, from 0.02 to 10.64 for a day of exposure. The impact of the exposure on the time trend in the number of births after the Super Bowl is also negligible.

[Table 7 about here.]

PRELIMINARY CONCLUSIONS

In this paper, we challenged the assumption made by the NFL in a highly publicized

Super Bowl 2016 commercial that winning cities experience an increase in births nine months after the Super Bowl. Using stadium locations and county data as proxies for teams’ cities, we analyzed the number of births in counties of teams that won or lost Super Bowl and even expanded our tests of the commercial to include exposure effects of the NFL playoffs. We looked for immediate effects of nine Super Bowls, corresponding to the 2003-2011 seasons, on the number of births nine months after the Super Bowl night (i.e., in the following October).

11 Super Bowl Babies

Additionally, we looked for a long-term effect of the Super Bowl night by examining changes in the time trend of births in the respective counties after each respective October.

Our results provide evidence that there is no clear pattern of a positive bump in the number of births in the counties of winning teams nine months after the Super Bowl. For eight out of nine NFL seasons, counties of the winning teams record a higher than monthly average number of births in October of the year the team won the Super Bowl. However, these spikes are small and fall within one standard deviation from the average number of births for seven out of nine NFL seasons. A similar pattern was observed when we compared the October of the year of the win to nine consecutive Octobers from 2003-2011: small spikes were apparent but they did not deviate much not from the average.

We also show that the change in the births in October after the Super Bowl and the change in trends in births after October do not vary between winners and losers for the Super

Bowl: neither winners nor losers experience a marked spike or drop in the births after the Super

Bowl. Finally, we show that the effects of the Super Bowl do not vary by exposure to the NFL playoffs – none of the counties for the teams participating in playoffs from 2003 to 2011 record a marked change in the number of births in September following the playoffs their teams took part in. Altogether, these results cast doubt on the claim that the Super Bowl affects births nine months later. We have tested this using winning teams, losing teams, and even playoff contenders, but no effects appear.

There are several possible explanations for why we did not find the birth spikes that the

NFL proposes in the commercial. First, we may have simply lacked statistical power. The CITS models suffer dramatically from the lack of power and the inconsistency of the estimated coefficients across years. With only one team winning and twelve teams participating in the

12 Super Bowl Babies playoffs each year, we might not have sufficient power to detect statistical significance. The estimates from the CITS are also not consistent across years. The instability of the estimates could further suggest that there exists no consistent population level effect of the Super Bowl euphoria. The problem of the small sample size could be remedied by using a synthetic control method or ARIMA models that would analyze in more detail time series of the births for each of the counties. Yet we believe that the small difference between the month of the treatment and average number of births in the analyzed counties does not provide sufficient evidence to justify investing time in these more complex methods.

Second, we have may be limited by the quality of our data. To truly link the effect of the

Super Bowl with fertility in winning cities, the analysis of the number of conceptions following the win would be more appropriate. A difference in conceptions immediately after the win provides stronger evidence that it was indeed the win that affected the fertility trends and not other events. A bump in birth rates, even nine months later, cannot be exactly linked with the

Super Bowl win since some other events might have followed. Perhaps a few days or weeks later there was another event in the winning cities that had a similar effect. This would certainly blur the relationship between the Super Bowl win and the number of births.

Finally, it is possible that any Super Bowl effect is concentrated within smaller populations of diehard fans. A Super Bowl win is certainly a happy and exiting experience for the fans of the winning team. However, many citizens do not follow nor care much about their local professional sports teams. It is unlikely that individuals like these would be affected by a

Super Bowl victory. If the victory affects only fans and not the entire population, even a positive effect of winning might not be large enough to register a change in the trends for the total

13 Super Bowl Babies population of the city. For the win to have a sufficient effect, one needs to assume that the victory affects a sufficiently large population.

In sum, while the Super Bowl commercial has undoubtedly sparked thousands of interesting, and potentially awkward, conversations among fans and viewers alike, we do not find evidence that births are associated with Super Bowl wins, losses, or even participation in the playoffs. Meanwhile, the commercial lives on, and has amassed nearly five million views at the time of this writing. It will be interesting to see whether the commercial, rather than the game, has an effect on fertility; perhaps it has planted an idea that viewers did not previously have.

14 Super Bowl Babies

REFERENCES

Centers for Disease Control and Prevention. 2016a. “Natality Data Summary.” CDC WONDER. Retrieved September 1, 2016 (http://wonder.cdc.gov/wonder/help/Natality.html#).

Centers for Disease Control and Prevention. 2016b. “Natality Information: Live Births.” CDC WONDER. Retrieved September 1, 2016 (http://wonder.cdc.gov/natality.html).

Committee on Fetus and Newborn. 2004. “Age Terminology During the Perinatal Period.” Pediatrics 114(5):1362–64.

Jukic, A. M., D. D. Baird, C. R. Weinberg, D. R. McConnaughey, and A. J. Wilcox. 2013. “Length of Human Pregnancy and Contributors to Its Natural Variation.” Human Reproduction 28(10):2848–55.

Martin, Joyce A., Brady E. Hamilton, Michelle J. K. Osterman, Sally C. Curtin, and T. J. Mathews. 2015. Births: Final Data for 2013. Centers for Disease Control and Prevention. Retrieved (http://www.cdc.gov/nchs/data/nvsr/nvsr64/nvsr64_01.pdf).

Montesinos, Jesus, Jordi Cortes, Anna Arnau, Josep A. Sanchez, Matt Elmore, Narcis Macia, José A. Gonzalez, Ramon Santisteve, Erik Cobo, and Joan Bosch. 2013. “Barcelona Baby Boom: Does Sporting Success Affect Birth Rate?” British Medical Journal 347(7938). Retrieved (http://www.bmj.com.libproxy.lib.unc.edu/content/347/bmj.f7387).

NFL. 2016a. “Super Bowl Babies Choir” Feat. Seal | Music Video. Retrieved March 3, 2016 (https://www.youtube.com/watch?v=9KqekigARfE&spfreload=10).

NFL. 2016b. Super Bowl Babies Choir Feat. Seal | Super Bowl 50 Commercial. Retrieved March 3, 2016 (https://www.youtube.com/watch?v=9KqekigARfE&spfreload=10).

NFL Enterprises. 2016. “Schedules.” Retrieved (http://www.nfl.com/schedules).

Shadish, William, Thomas D. Cook, and Donald T. Campbell. 2002. Experimental and Quasi- Experimental Designs for Generalized Causal Inference. Boston: Houghton Mifflin.

Udry, J. Richard. 1970. “The Effect of the Great Blackout of 1965 on Births in New York City.” Demography 7(3):325–27.

15 Table 1. NFL Teams, Stadium Locations, and Counties Used in Analyses Team Stadium Stadium Location County Used Arizona Cardinals University of Phoenix Stadium Glendale, Arizona Maricopa County Atlanta Falcons Georgia Dome Atlanta, Georgia Fulton County Baltimore Ravens M&T Bank Stadium Baltimore, Maryland Baltimore County Buffalo Bills New Era Field Orchard Park, New York Erie County Carolina Panthers Bank of America Stadium Charlotte, North Carolina Mecklenburg County Chicago Bears Soldier Field Chicago, Illinois Cook County Cincinnati Bengals Paul Brown Stadium Cincinnati, Ohio Hamilton County Cleveland Browns FirstEnergy Stadium Cleveland, Ohio Cuyahoga County Dallas Cowboys AT&T Stadium Arlington, Texas Tarrant County Denver Broncos Sports Authority Field at Mile High Denver, Colorado Denver County Detroit Lions Ford Field Detroit, Michigan Wayne County Green Bay Packers Lambeau Field Green Bay, Wisconsin Brown County Houston Texans NRG Stadium Houston, Texas Harris County Indianapolis Colts Lucas Oil Stadium Indianapolis, Indiana Marion County Jacksonville Jaguars EverBank Field Jacksonville, Florida Duval County Kansas City Chiefs Arrowhead Stadium Kansas City, Missouri Jackson County Miami Dolphins Sun Life Stadium Miami Gardens, Florida Miami-Dade County Minnesota Vikings U.S. Bank Stadium Minneapolis, Minnesota Hennepin County New England Patriots Gillette Stadium Foxborough, Massachusetts Norfolk County New Orleans Saints Mercedes-Benz Superdome New Orleans, Louisiana Orleans Parish New York Giants2 MetLife Stadium East Rutherford, New Jersey Bergen County New York Jets2 MetLife Stadium East Rutherford, New Jersey Bergen County Oakland Raiders Oakland Alameda Coliseum Oakland, California Alameda County Philadelphia Eagles Lincoln Financial Field Philadelphia, Pennsylvania Philadelphia County Pittsburgh Steelers Heinz Field Pittsburgh, Pennsylvania Allegheny County San Diego Chargers Qualcomm Stadium San Diego, California San Diego County San Francisco 49ers Levi's Stadium Santa Clara, California Santa Clara County Seattle Seahawks CenturyLink Field Seattle, Washington King County St. Louis Rams1 The Dome at America's Center St. Louis, Missouri St. Louis County Tampa Bay Buccaneers Raymond James Stadium Tampa, Florida Hillsborough County Tennessee Titans Nissan Stadium Nashville, Tennessee Davidson County Super Bowl Babies

Washington Redskins FedEx Field Greater Landover, Maryland Prince George's County Sources: NFL.com, individual NFL® team websites, and Google Maps. Notes. All counties listed correspond to stadium locations. On some occasions, the team name suggests a location that does not correspond to stadium location (i.e., the Dallas Cowboys' stadium is not in Dallas County). 1The St. Louis Rams became the Los Angeles Rams in 2016, but there are referred to here by their name during the years of interest. Accordingly, their stadium and county apply to their St. Louis location. 2The New York Giants and the New York Jets share a home stadium.

1 Super Bowl Babies

Table 2. Approximate Dates of Birth for Super Bowl Babies by Length of Pregnancy Weeks1 Season Super Bowl Date Winner Loser 35/37 36/38 37/39 38/40 39/41 2003 38 2/1/04 New England Patriots Carolina Panthers 10/3/04 10/10/04 10/17/04 10/24/04 10/31/04 2004 39 2/6/05 New England Patriots Philadelphia Eagles 10/9/05 10/16/05 10/23/05 10/30/05 11/6/05 2005 40 2/5/06 Pittsburgh Steelers Seattle Seahawks 10/8/06 10/15/06 10/22/06 10/29/06 11/5/06 2006 41 2/4/07 Indianapolis Colts Chicago Bears 10/7/07 10/14/07 10/21/07 10/28/07 11/4/07 2007 42 2/3/08 New York Giants New England Patriots 10/5/08 10/12/08 10/19/08 10/26/08 11/2/08 2008 43 2/1/09 Pittsburgh Steelers Arizona Cardinals 10/4/09 10/11/09 10/18/09 10/25/09 11/1/09 2009 44 2/7/10 New Orleans Saints Indianapolis Colts 10/10/10 10/17/10 10/24/10 10/31/10 11/7/10 2010 45 2/6/11 Green Bay Packers Pittsburgh Steelers 10/9/11 10/16/11 10/23/11 10/30/11 11/6/11 2011 46 2/5/12 New York Giants New England Patriots 10/7/12 10/14/12 10/21/12 10/28/12 11/4/12 Notes. All shaded cells indicate October births. 37-38 weeks of gestation are considered early term, 39-40 are considered full term, and 41 weeks is considered late term (Martin et al. 2015). According to CDC estimates from 2005-2013, between 81.44 and 83.13 percent of all births will fall within the range of gestational ages presented here. 1The first number represents weeks from conception and the second represents gestational age (Committee on Fetus and Newborn 2004).

2 Super Bowl Babies

Table 3. Approximate Dates of Birth for Babies Conceived During the Playoffs by Length of Pregnancy Weeks of Conception and Gestation 1 Playoffs 35/37 36/38 37/39 38/40 39/41 Season Start End2 Births range Births range Births range Births range Births range 2003 12/28/03 1/18/04 8/29/04 9/19/04 9/5/04 9/26/04 9/12/04 10/3/04 9/19/04 10/10/04 9/26/04 10/17/04 2004 1/2/05 1/23/05 9/4/05 9/25/05 9/11/05 10/2/05 9/18/05 10/9/05 9/25/05 10/16/05 10/2/05 10/23/05 2005 1/1/06 1/22/06 9/3/06 9/24/06 9/10/06 10/1/06 9/17/06 10/8/06 9/24/06 10/15/06 10/1/06 10/22/06 2006 12/31/06 1/21/07 9/2/07 9/23/07 9/9/07 9/30/07 9/16/07 10/7/07 9/23/07 10/14/07 9/30/07 10/21/07 2007 12/30/07 1/20/08 8/31/08 9/21/08 9/7/08 9/28/08 9/14/08 10/5/08 9/21/08 10/12/08 9/28/08 10/19/08 2008 12/28/08 1/18/09 8/30/09 9/20/09 9/6/09 9/27/09 9/13/09 10/4/09 9/20/09 10/11/09 9/27/09 10/18/09 2009 1/3/10 1/24/10 9/5/10 9/26/10 9/12/10 10/3/10 9/19/10 10/10/10 9/26/10 10/17/10 10/3/10 10/24/10 2010 1/2/11 1/23/11 9/4/11 9/25/11 9/11/11 10/2/11 9/18/11 10/9/11 9/25/11 10/16/11 10/2/11 10/23/11 2011 1/1/12 1/22/12 9/2/12 9/23/12 9/9/12 9/30/12 9/16/12 10/7/12 9/23/12 10/14/12 9/30/12 10/21/12 Notes. Light shading indicates September births and dark shading indicates October births. 1The first number represents weeks from conception and the second represents weeks from gestation. 2End refers to the end date for all teams other than the Super Bowl contenders.

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Table 4. Number of County Births Following Super Bowl Victories Compared to Mean Numbers of County Births Mean of Super October Mean of Standardized October Standardized Season Bowl Winner Births1 Births2 (SD) Difference Difference Births3 (SD) Difference Difference 2003 38 New England Patriots 659 665 (54) -6 -0.11 634 (21) 25 1.16 2004 39 New England Patriots 657 644 (44) 13 0.28 634 (21) 23 1.07 2005 40 Pittsburgh Steelers 1,016 1,100 (58) -84 -1.46 1,096 (44) -80 -1.81 2006 41 Indianapolis Colts 1,343 1291 (67) 52 0.76 1,254 (64) 89 1.40 2007 42 New York Giants 821 809 (57) 12 0.22 816 (40) 5 0.11 2008 43 Pittsburgh Steelers 1,099 1,087 (72) 12 0.17 1,096 (44) 3 0.06 2009 44 New Orleans Saints 383 385 (33) -2 -0.06 380 (131) 3 0.02 2010 45 Green Bay Packers 271 283 (23) -12 -0.53 283 (19) -12 -0.61 2011 46 New York Giants 799 767 (51) 32 0.63 816 (40) -17 -0.44 1October births following the Super Bowl victory. 2Mean of births for 37 months (the October following the Super Bowl and 18 months before and after). 3Mean of births for all 9 Octobers (one corresponding to each season of interest).

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Table 5. Comparisons of County Births for Counties with Winning and Losing Super Bowl Teams Season 2003 2004 2005 2006 2007 2008 2009 2010 2011 Winning team Patriots Patriots Steelers Colts Giants Steelers Saints Packers Giants Winners' baseline differences in births -286.31 *** -902.29*** -451.29 * -4993.92*** 459.82 * -5643.87*** -1293.78 *** -468.37 25.69 Time trend in births for losers 2.79 8.62* 12.93 ** 7.12 0.80 -24.07 -4.73 4.07 -1.43 Spike/drop in births for losers -76.84 -33.89 330.89 1427.78 -81.85 -102.76 -254.23 4.15 -83.55 Time trend in births for winners -6.99* -11.20 * -11.56 * -7.86 -4.49 24.00 4.99 -3.58 1.24 Difference in births for winners1 -38.47 -50.86 -488.06 -1396.52 33.58 9.62 368.29 120.86 64.52 Change in time trend for losers 2.51 -1.36 -8.87 -27.15 0.14 0.47 2.78 -0.86 0.76 Difference in time trend for winners1 1.56 3.83 11.28 26.56 0.67 0.04 -3.84 -0.36 -0.63 Constant 1037.96 *** 1623.28*** 1500.87 *** 6332.74 *** 599.24 *** 6760.74*** 1650.95*** 707.78 *** 764.36*** AIC 784.70 856.85 876.02 1032.13 789.40 1009.06 815.98 797.65 787.81 Number of months1 74 74 74 74 74 74 74 74 74 1Compared to losers. 2Each of the two teams has 37 months of observations, totaling to 74. *p<.05; **p<.01; ***p<.001

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Table 6. Difference between the Spike/Drop for Winners in October and the Average Births for Each Respective Period of Analysis (37 months) Season 2003 2004 2005 2006 2007 2008 2009 2010 2011 Winning Team Patriots Patriots Steelers Colts Giants Steelers Saints Packers Giants Total spike/drop for the winners -115 -85 -157 31 -48 -93 114 125 -19 Average births by month for winners 665 644 1100 1291 809 1087 385 283 767 Percent difference -17% -13% -14% 2% -6% -9% 30% 44% -2%

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Table 7. Effects on Births by Exposure to the Playoffs Season 2003 2004 2005 2006 2007 2008 2009 2010 2011 Exposure effect on births1 -6.04 0.33 -4.05 10.64 -2.46 -5.17 -0.02 -7.28 8.65 Exposure effect on the time trend2 0.23 -0.03 0.06 -0.23 0.04 0.07 0.00 0.07 -0.07 1Effects of exposure on births in the Septembers following the Super Bowl. 2Effects of exposure on the change in the time trend after the Super Bowl.