Major League Baseball and Crime: Opportunity, Spatial Patterns, and Team Rivalry at St

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Major League Baseball and Crime: Opportunity, Spatial Patterns, and Team Rivalry at St. Louis Cardinal Games

Article in Journal of Sports Economics · January 2019 DOI: 10.1177/1527002518822702

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Published in: Journal of Sports Economics

DOI: 10.1177/1527002518822702

https://journals.sagepub.com/doi/full/10.1177/1527002518822702

Major League Baseball and Crime: Opportunity, Spatial Patterns, and Team Rivalry at St.

Louis Cardinal Games

Dennis Mares

Emily Blackburn

Abstract

Hosting professional sports teams is often seen as a financial benefit for cities. In the following analysis, we provide evidence that sports teams also carry costs. The analysis, the first examining a major-league baseball team, finds significant increases in a variety of crimes during home game days of the St. Louis

Cardinals. Adjusting for attendance and game length, this study finds that larcenies, motor vehicle thefts, minor assaults, disorderly conduct and destruction of property increase in volume during game days.

Increases concentrate especially around the immediate stadium area, but some are also observable in city- wide levels of crime. Additionally, this study examines differences between the time of day a game is played, and games played against its historic rival, the Chicago Cubs.

Keywords: Baseball, MLB, St. Louis, Crime, Team Rivalry

2

Introduction

Recent interest in the economic benefits of major sports teams increasingly focuses on the deleterious economic effects of crime during major sports events. Several studies, for instance, have linked football and soccer to increasing crime rates. Major League Baseball (MLB) has generally been overlooked in such studies (Vermillion, Stoldt, & Bass, 2009). Larrick, Timmerman, Carton, and Abreya (2011), note a relation between warm temperatures and the likelihood of a pitcher hitting a batter, but no studies examine if the game event itself may have an impact on crime. Perhaps the reason for this gap in the literature rests on the notion that baseball is considered a family friendly spectator sport, not associated with rioting sometimes present during soccer or college football games (Seff, 2015). MLB games may primarily increase crime because large numbers of people and their possessions increase opportunities for crime.

The following study is the first to systematically evaluate the link between a MLB team and a wide range of offenses. Using nearly 23 years of daily data from St. Louis, MO we examine how crimes are likely to change during game days. We explore crimes in which the spectators are likely perpetrators

(disorder offenses), and crimes in which the spectators are likely victims (pecuniary offenses). The study incorporates both attendance and game length to gauge the elasticity of crime during game days, and additionally explores the spatial dynamics of crime changes during game days by comparing the stadium area to city wide changes. Results indicate that in St. Louis, MLB games are likely responsible for increases in a variety of serious property crimes (larceny and motor vehicle theft) as well as increases in disorder related offenses (minor assault, disorderly conduct and vandalism).

Prior Quantitative Research on Sports and Crime Levels

The relatively small body of quantitative literature on the link between crime and professional sports primarily focuses on (American) Football and soccer. In a study of 26 college football towns, for 3

example, Rees and Schnepel (2009) examine if college football games are tied to increases in criminality.

The authors study daily data over a period of six years. Reporting on both home and away games, Rees and Schnepel conclude that home games are associated with a 9% increase in assaults an 18% increase in vandalism, a 13% increase in DUIs, and a substantial 41% uptick in arrests for disorderly conduct. Away games do not affect crime levels, but home game losses appear to increase crime levels more so than home game wins.

In a six-city sample of National Football League (NFL) teams, Card and Dahl (2011) find that upset losses are associated with a 10% increase in domestic assaults in those cities. When the home team wins, or expectations are exceeded, no such criminogenic effects are detected. Card and Dahl’s study shows that crime may not just increase directly near the site of the game, but that spectators in a variety of locations may be impacted. Using annual metropolitan crime data, Baumann, Ciavarra, Englehardt, and

Matheson (2012) examine if the presence of professional sports teams is associated with increases in either property or violent crimes; the results indicate no association between the presence of sports teams and higher crime levels. One may, however, question whether annual data are sufficient and distinct enough to show such patterns. Using a natural experiment, the Detroit Lions relocation in 2002, Pyun and

Hall (2016) study levels of assaults, larceny, auto thefts and vandalism before and after the team’s relocation. Results of this study suggest that after the exit of the Lions larcenies experienced a significant reduction. In a more recent study, Pyun (2018) studies the move of the Washington Nationals to

Washington DC in 2005. Using a triple difference-in-difference approach Pyun (2018) uncovers a significant increase in assaults as a result of the move. In perhaps the most comprehensive examination of professional NFL games and crime, Kalist and Lee (2014) examine three years of daily crimes in eight cities and find that home games are associated with a 2.6 % increase in total crimes. Larcenies and motor vehicle thefts are hit particularly hard with increases of 4.1 and 6.7% respectively. The authors acknowledge, however, that these results reflect city wide crime increases, and may not reflect what happens near the stadiums. Using data from the 1990 World Soccer Cup in Italy, Campaniello (2011) 4

examines the impact of mega sporting events on crime. Results indicate that provinces hosting such games experience an increase in pick pocketing, shoplifting and burglary. In a more detailed study of soccer matches in London boroughs, Marie (2016) finds a significant impact based on attendance levels, equaling about a four percent increase in crime for each 10,000 attendees at a soccer match.

Disaggregating crime levels further, Marie finds no support for increases in violent crime, yet details a significant increase in property offenses. Additionally, in an examination of soccer matches in

Montevideo, Uruguay, Munyo and Rossi (2013) examine the impact of upset wins and losses on property and violent offenses. Upset losses significantly increase robberies, whereas upset wins decrease robberies, indicating that the outcome of matches may be important.

Most prior studies examine city crime levels, but Breetzke and Cohn (2013) also explore spatial dimensions of sporting games, comparing crime level changes at multiple distances from a stadium.

Breetzke and Cohn (2013) study roughly five years of sporting events (soccer and rugby) in a South

African city. Results indicate that 50 more crimes occur on days when the home team wins, whereas 30 additional crimes occur on average if the home team loses. Interestingly, Breetzke and Cohn find that assaults and drunk and disorderly incidents increase up to a one-mile radius around the stadium but find no significant effects city-wide. This finding may indicate that the scale of geography matters. Billings and Depken (2012) report similar findings during NFL and NBA games in Charlotte, NC. Areas immediately surrounding the sports venues see marked increases in both violent and property offenses.

Interestingly, these authors report a decline in crime in areas between one and two miles away from the stadia. Studies that only examine city level data may risk ‘washing’ out results because the increases around a stadium area may not be large enough to significantly alter a city’s ‘natural’ variation in crime levels. They also risk not identifying geographic displacements in crime (Billings and Depken, 2012).

This is particularly relevant in a US setting where political fragmentation of urban boundaries means researchers are studying small and large cities as equivalent. The predicted impacts of professional sports teams on city crime levels may thus -in part- reflect the population size of the municipality. 5

A few conclusions can be drawn from prior research. Most studies report a positive association between some crime categories and sporting events. The strongest support for such a link is among property offenses (such as vehicle thefts) and less-serious aggressive behaviors (such as minor assaults and disorderly conduct). There is little evidence that serious assaults or burglaries are influenced by game days. Results appear stronger for smaller geographic areas and there may be a decay as one moves further away from the immediate stadium area. Finally, studies using daily data appear to find the strongest and most consistent associations between sports events and crime. The results thus strongly speak to the importance of the level of aggregation in the data with the most refined data showing the most consistent positive findings.

Absent in current research is a more thorough assessment of Major League Baseball (MLB) games. As indicated, most studies thus far have focused on Football (US) or soccer (international), but few have ventured beyond. Baseball is an ideal candidate for study. The sport is generally considered a more family friendly spectator affair with little documented violent fan rivalry. Spectator property victimization may thus be a more pressing issue at MLB games, which draw in large crowds many times throughout the year. MLB teams have an exhaustive annual schedule, typically playing 81 home games and 81 away games over a six-month period. Such a busy schedule reduces the chances that any found association between game days and crime is accidental, or the result of concurrent stimuli.

Studies in the US have typically examined the impact of sports games on city or metropolitan crime rates but have generally failed to examine the immediate area around a stadium. Both Breetzke and

Cohn (2013) and Billings and Depken (2012) illustrate the importance of geographical scale. Within a one-mile radius around the stadium the authors of both studies find strong crime increases during game days, but results turn non-significant or sometimes even negative beyond this geographic scale. It is quite probable that a large population base absorbs game day crime increases within typical daily city crime fluctuations. One may wonder if a study with a higher number of games and more specific crime 6

categories located in more moderate sized city would yield a clearer picture how game days may help shape both localized and city level crime rates.

Theoretical Backdrop: Spectators as Offenders or Victims?

Positive association between sports games and higher crime levels can be explained using numerous pathways. Fan rivalry, alcohol consumption, and disappointment over game outcomes may all be considered motivational factors explaining increased frustration, irritability and aggression. For instance, while it may not be shocking to hear that spectators at sporting events in the US are likely to consume alcohol, one study finds that in a breathalyzer sample of males aged 20-35 attending a MLB game 60 percent showed evidence of alcohol consumption. Moreover, by the fifth inning thirteen percent of the sample was legally impaired (Wolfe, Martinez, & Scott, 1998). Higher levels of alcohol consumption of spectators in MLB games may be linked to increased aggressive behavior, as there is a well-documented link between alcoholic intake and criminal behavior, including assaultive behavior, property damage and vandalism (Boden, Fergusson, & Horwood, 2013; Ostrowsky, 2014).

Violent and aggressive behavior has been extensively observed in European soccer, but far less in the US (Marie, 2016; Munyo & Rossi, 2013; Roberts & Benjamin, 2000; Ward, 2002). Ward (2002) suggest lower levels of violence in the US are the outcome of a lower prominence of soccer in professional sports, and a more economically and gender diverse crowd of spectators. Somewhat similarly, Roberts and Benjamin (2000) believe that the lower incidence of spectator violence reflects the popularity of specific sports. North American sports, such as football and hockey are characterized by high levels of (virtually) unpunished violence on the field; whereas soccer games highly regulate violent player behavior. In European soccer, spectators’ allegiances to any sporting teams are typically determined by a person’s urban roots. US professional teams typically have a shorter history and are often not as tied down to locality. As Roberts and Benjamin state (2000 p. 170): “The idea …of Manchester 7

United being ‘sold’ to another city is … unthinkable in the United Kingdom, although it happens frequently in North America.” Indeed, the typical absence of deep team rivalries in the US may be an outcome of the mobility of professional sports teams. Nonetheless even in the US, violence and aggression have at least been common enough to trigger the development of a problem-oriented policing guide for security providers at stadiums (Madensen & Eck, 2008). The exact psychological mechanisms through which spectators may become aggressive or violent are not well understood and a variety of theoretical approaches have been put forward (Ward, 2000; Ostrowsky, 2014). Regardless, team rivalry, frustrations with game outcomes, social exclusion and other socioeconomic and psychological factors may well explain aggressive spectator behavior at games. Such explanations, however, are insufficient to determine why property victimization (such as larceny and motor vehicle thefts) also appears to increase during game days.

Routine activities theory (RAT) provides an explanatory framework that explores the opportunity structure for crimes. Developed by Cohen and Felson (1979), RAT is a useful theoretical approach to explain sometimes contradictory findings (Carbone-Lopez and Lauritsen, 2013). Cohen and Felson themselves sought to explain why property offenses in the US increased in the 1970s even though poverty was declining. Homes, the authors explain, became increasingly suitable targets for burglaries as supervision was reduced due to increasing female participation in the labor market and growing suburbanization, which reduced neighbor supervision. According to Cohen and Felson (1979) crimes occur when three elements fuse in time and space: a motivated offender, a suitable target and the absence of a capable guardian. In economic terms, demand for crime may be elastic depending on the supply of opportunities. Studying macro level data, Levitt (1996) argued incarceration may limit the supply of offenders and thereby reduce crime. RAT, however, focuses more at micro level interactions and how changes in crime opportunities may result in changes in crime levels; this may include changes in the number of potential offenders, but may also be due to changes in the number of easily available targets.

During professional sports games, spectators and their property increase the volume of suitable targets. 8

Vehicles sit unattended during games, creating a large reservoir of suitable targets. Given that many spectators live outside the stadium area they may also not be very familiar with local crime patterns and thus may themselves become victims of theft or robbery. Alcohol use may further enhance such risks as it reduces a person’s situational awareness. RAT is a theoretical perspective with a substantial track record in criminological research and has been applied extensively in time-series and spatial analysis to explain crime variation (Cohn & Breeztke, 2013; Mares & Moffett, 2016).

Taking into consideration empirical research on crime increases during professional sports, here we consider how MLB games may impact crime levels. Given that soccer fan violence appears to be driven by deep historical urban roots of teams, MLB games may be at some risk to produce higher rates of spectator-driven aggressive behavior. Baseball has the distinction of being one of the oldest professional sports in the US with many teams having roots in the 19th century. Few teams have moved during those years, which may indicate why some historical team rivalries exist in the MLB (Reed, 2017).

Combined with evidence of excessive alcohol consumption that may lower inhibitions and self-control among spectators (Wolfe, Martinez, & Scott, 1998) an increase in aggressive spectator behaviors may be expected during game days. With respect to aggressive fan behavior, RAT would suggest that alcohol consumption, and game emotions may increase offender motivation, whereas large crowd sizes provide ample opportunities to find suitable targets.

Spectators are probably not perpetrators of pecuniary offenses, but their property are likely targets within the specific opportunity structure that game days present. MLB teams attract an average of 30,000 spectators per game, a substantial number of which travel by vehicle (Humphreys & Pyun, 2018). Such an influx of people in a concentrated area arguably heightens opportunities for would-be offenders. A typical

MLB game lasts nearly three hours (Caple, 2013), during which parking garages and streets are virtually abandoned. Many attendees may not take typical precautions that many urban parkers take, such as putting valuables out of sight. Latecomers often find themselves parking in fringe areas with even less supervision. We suspect that property offenses, especially those focused on vehicles (larceny and motor 9

vehicle theft) are likely to increase during MLB game days. Additionally, we also suspect that robberies may increase. Prior research on basketball games indicates that robberies increase during such event (Yu et. al., 2016). Individuals, unfamiliar with an area and potentially inebriated, may make suitable targets for a robbery. In other words, more traditional street crimes focused on pecuniary gain are likely to increase because a greater number of targets present themselves in a highly concentrated area.

We expect that property crime levels and minor aggressive offenses in the immediate area around an MLB stadium will increase during game days, but we are unsure how MLB games may impact city levels of crime. All things equal, a substantial increase around the stadium area is likely required to significantly alter city wide crime levels in a high crime city such as St. Louis. Additionally, even a strong increase in the stadium area may not lift city crime levels if local offenders purposively travel to the stadium area to commit offenses. In other words, crime may simply be internally displaced.

Given the greater degree of historical team rivalry in MLB we expect that crime levels may alter depending on which team is played. In the case of a team rival we expect that fan emotions may provide additional fuel for increases in aggressive behavior, but we expect little difference in pecuniary offenses

(presuming the win-loss ratios are similar, and one holds constant game length and attendance size).

We additionally consider when a game is played. Games played during evening hours may provide an increase in opportunities as darkness may reduce guardianship. We expect this to be especially the case for property offenses but suspect it may make little difference for aggressive fan behavior.

It is important to note that the Cardinals hire off-duty police officers to provide additional security during game days, which means there is a net-increase in police in the immediate stadium area and no reductions in coverage in the rest of the city. It thus stands to reason that police resources by themselves are not a likely source of any crime increases we may find.

Data and Methods 10

Given the absence of empirical knowledge about MLB games and their impact on crime rates, the current study examines nearly 23 years (January 1st, 1994 through June 30th, 2016, n=8,217) of crime data from

St. Louis, MO, including 1,773 days on which at least one home game was played. Here we study a variety of crime categories near the St. Louis Cardinals’ Busch stadium and the city of St. Louis. Further, we examine the likely influence of team rivalry as well as the time of day a game is played.

St. Louis is chosen for several reasons. First, St. Louis has a relatively high base crime rate, regardless of MLB games. This allows us to examine some smaller geographical areas as daily data are reasonably populated. Second, St. Louis hosts a leading MLB team with a long history and loyal local fan base, ensuring consistent high attendance. Typical attendance at Cardinals games is slightly above 40,000 per game. The Cardinals have been in St. Louis for a substantial time and although they constructed a new stadium in 2006, that switch should have had little impact since the new stadium was built on roughly the same footprint.

Data are retrieved from multiple sources. Dependent data, daily crime counts, were collected from the St. Louis Metropolitan Police Department (SLMPD). The data are Uniform Crime Reporting

(UCR) quality controlled ensuring a degree of consistency in recording and coding practices. All major crime categories (UCR Part I) were examined in the preliminary stages of this study, but we only present offenses that were reported with a reasonable daily frequency. Rape and homicide occur rarely at the daily level and thus are excluded. The following crime categories are examined: five individual UCR Part

I offenses: robbery, aggravated assault, burglary, larceny, and motor vehicle theft; three specific Part II offenses: simple assault, disorderly conduct, and vandalism (sometimes referred to as destruction of property, or criminal damage); and a combined UCR Part I category. Readers interested in the specific categories of behavior included may consult the FBI’s website for specific definitions

(https://ucr.fbi.gov/crime-in-the-u.s/2012/crime-in-the-u.s.-2012/offense-definitions). Although prior studies that incorporate multiple geographic levels (e.g., Billings & Depken, 2012; Breetzke & Cohn,

2013) use aggregate categories of crime, we focus mainly on disaggregated crime types. More specific 11

crime types allow us to better speak to the theoretical predictions and make a more accurate cost- assessment. Further, St. Louis’ relatively high crime ensures a reasonable frequency of most crime counts at the daily level in the stadium area.

Since we are interested in examining the specific area around the stadium a decision had to be made whether to use distance bands (see Billings & Depken, 2012; Breetzke and Cohn, 2013) or to incorporate an area that more aptly reflects mobility in an area. The decision was made to use official city neighborhood designations as they better cover the specific area in which fans would park vehicles and frequent bars and restaurants pre- and post-game. Distance bands also would not be complete as Busch stadium is located on the Eastern edge of the city limits, which means some distance bands would cross into the Mississippi river and neighboring East St. Louis, IL (see Figure 1). Natural borders, such as rivers, almost certainly impact movement of offenders and victims. In addition, geocoding address data in the stadium area proved especially problematic as quite a few ‘addresses’ include highway markers, onramps, buildings and bridges. Given the already low frequency of crime at the daily level we therefore fear using geocoded data introduces a potential downward bias on the stadium area. During preliminary research, we examined four neighborhoods in St. Louis: Downtown, a commercial district which contains all St. Louis area professional sports venues and is about a mile by half mile area; LaSalle, a mixed-use area just South of the MLB stadium; Columbus Square, just to the North of Downtown; and Downtown

West, a mostly commercial area just West of the stadium. Combined the area is about 1.5 miles across.

-Figure 1 about here-

Because prior studies find that daily crime counts are impacted by climatic variation, the current study includes a variety of weather data (see Cohn & Rotton, 2000, Mares, 2013, Mares & Moffett,

2016). Daily weather data from St. Louis Lambert Airport were obtained through NOAA’s Global

Historical Climatology Network. Lambert airport is located approximately ten miles from downtown St.

Louis and has the most consistent and complete reporting history of any weather station in the region. A variety of weather variables are included including daily maximum temperatures (Fahrenheit), a squared 12

temperature term to account for potential non-linear effects, total precipitation (inches), snow fall

(inches), snow on the ground (inches) and wind speed (miles per hour). It is important to acknowledge that the simple occurrence of various weather events may be as important as their relative size. The latter may be especially true for crimes as the occurrence of snow may keep people home (both victims and offenders), regardless of the size of the amount of snow falling. Therefore, additional variables are included to model the effects of the occurrence of snow, precipitation, fog, thunder and hail. Such weather events may limit the number of people traveling to and within St. Louis, or attending game days, thereby reducing opportunities for crime.

Additional control variables are generated to account for time varying influences on crime. Most daily crime studies incorporate binary indicators for federal holidays, for instance (Rotton & Cohn, 2003).

In addition, we generate binary variables for day of the week, day of the month, month, and year. Our models thus account for crime variation due to unmeasured time variation. Although day of the month binaries are rarely included in daily analysis, we contend that such a practice should be attempted when feasible. Police data often include some ambiguity on the date on which a crime occurred. In some cases, the date of occurrence is merely an estimate if the day of victimization is unknown by a victim, or a crime is reported well after the fact. Often such crimes get ‘dumped’ on the first, 15th, or last day of the month in which the crime is thought to have occurred. There may be other issues that introduce a systematic bias on crime reporting dates we may not be aware of; therefore, we incorporate a binary variable for each day of the month.

The Cardinals’ play schedule was retrieved from a website that post historical MLB schedules

(https://www.baseball-reference.com ). Included are the date of plays, the name of the opposing team, whether a play occurred home or away, whether the game was played during the daytime or nighttime and the outcome (win/lose). Additionally, attendance numbers and game duration are recorded. Both regular season and postseason games are included. Home game days for the two other professional sports teams in St. Louis, the Rams (NFL) and the Blues (NHL) act as control variables. We first generated binary 13

variables for all home games for MLB, NHL and NFL games (for analyses with the game day binary, and several variations, please examine the supplementary documentation). Next, we create an interactive variable that provides a proxy for the likely changes in crime opportunities created by MLB games. We, follow Marie (2016) who measures opportunity changes during soccer matches by multiplying the home game binary by the number of attendees. Unlike soccer matches, however, MLB game times are more fluid. Therefore, we incorporate both attendance and game length to approximate the proportionate changes in criminal opportunities during home MLB games.

Our resulting data set is comprised of daily observations between January 1st, 1994 and June 30th,

2016, and includes crime, climatic and game indicators. Complete descriptive statistics for all levels of analysis may be found in the included online supplementary excel file, but Table 1 shows the relative frequencies of crimes in the immediate stadium area on game and non-game days as well as game and climate indicators. Table 1 indicates that crimes are higher during game days, but the descriptives also reveal that climatic indicators are different because the MLB season occurs during the warmer months.

Therefore, a more thorough analysis is required.

-Table 1 about here-

Analysis and results

The analytical strategy of this study rests on time-series modeling. The distribution of many crime counts in our study is non-normal given the low frequency at which crimes occur at the daily level (see Table 1, for example). In the current case, daily crime counts typically follow a negative binomial distribution as for most dependent variables the standard deviation exceeds the mean. Because non-normally distributed data violate OLS requirements we use negative binomial generalized linear models (GLM) employing a log-link. Poisson estimators would be inappropriate because they rest on the absence of overdispersion, 14

resulting in incorrect standard errors, biasing significance tests (Cameron & Trivedi 2013). Negative binominal models, however, provide correct standard errors regardless of the presence of overdispersion.

퐿푛 퐸(퐶푟𝑖푚푒푡) = 휏푡 + 훽0 + 훽1푂푝푝표푟푡푢푛𝑖푡푦푡 + 훽2퐵푙푢푒푠퐻표푚푒푡 + 훽3푅푎푚푠퐻표푚푒푡 + 훽4푃푟푒푐𝑖푝𝑖푡푎푡𝑖표푛푡 +

훽5푆푛표푤푓푎푙푙푡 + 훽6푆푛표푤푑푒푝푡ℎ푡 + 훽7푇푒푚푝푒푟푎푡푢푟푒푡 + 훽8푇푒푚푝푒푟푎푡푢푟푒2푡 + 훽9푊𝑖푛푑푠푝푒푒푑푡 + 훽10퐻푎𝑖푙푡 +

훽11퐹표푔푡 + 훽12푇ℎ푢푛푑푒푟푡 + 휀 (1)

Equation 1 describes that the expected level of Crime at day (t) is the predicted outcome of temporal binary controls (τ), including Holidays, days of the week, days of the month, month and year; weather controls, such as temperature, precipitation and binary variables that that may impact game attendance

(hail, fog etc.), and a squared temperature term to account for non-linear temperature effects; St. Louis

Rams home games (RamsHome) and St. Louis Blues home games (BluesHome). “Opportunity” is an interactive variable generated by the following process (1):

(퐴푡푡푒푛푑푎푛푐푒∗퐺푎푚푒 퐿푒푛𝑔푡ℎ) 푂푝푝표푟푡푢푛𝑖푡푦 = 퐻표푚푒 푔푎푚푒푑푎푦 ∗ ( ) (2) 푡 1,000,000

“Opportunity” estimates the elasticity of crime during MLB games as a result of changing crime opportunities at day (t) which are measured as the result of a binary process in which the Cardinals either play a home game (1) or do not play a home game (0) multiplied by attendance and game length (in minutes). “Opportunity” is a proxy of changes in crime opportunities, as not every additional attendee or minute of game creates an actual crime opportunity. Rather we would argue that RAT predicts that crime increases proportionately with increasers in the number of attendees and the length of a game. We scale our proxy for opportunity in millions of attendee-minutes in order to produce coefficients that are comparable to typical control variables. Our variable Opportunity thus provides a way to approximate proportionate changes in suitable targets during Cardinal home games. Exponentiated coefficient values for “Opportunity” reported in the tables may be read as the approximate proportionate change in crime counts for each additional million attendee-minutes. Because an average game generates about 6.94 15

million attendee-minutes, this number may be used as a multiplier to estimate average home game impacts to compare the current study to more traditional studies that use a binary home game day coefficient. Incorporating elements of the opportunity structure of an event allow one to more accurately model the approximate influence of changes in offenders, victims or guardianship (Bursik & Grasmick,

1993; Cohen & Felson, 1979) and thus provide a greater insight in the elasticity of crime during sports events. Prior studies have included stadia attendance data in their estimations, but none -to our best knowledge- have included game length (Marie, 2016). By including game length alongside attendance, results for different sports games (i.e., soccer, baseball, hockey) can be more directly compared as one standardizes the difference in time and attendance. Indeed, this approach may be replicated for non-sports events (fairs, concerts, and so forth). “Opportunity” should thus be seen as a proxy of the variations in criminal opportunities allowing us to examine if crime displays elasticity at micro levels. We must acknowledge that both attendance and game length may not be entirely representative of proportionate changes in opportunities. Games that are sold out may attract people to sporting venues above and beyond seating capacity and may fill local bars and restaurants. Additionally, game attendees may spend substantial time before, and after games in the stadium area.

There are two key concerns in contending with time-series data. First, unit roots are commonly found in time-series aggregated by month or year. In daily crime data unit roots occur more infrequently, as data tend to vary more randomly day-to-day. Not surprisingly, unit root tests rejected (Augmented

Dickey Fuller and Phillip-Perron) the presence of a unit root in any of the dependent series presented

(results available upon request). Second, time-series data often exhibit serial dependence, a violation of

OLS modeling. In GLM Poisson and Negative Binomial models, serial dependence does not constitute as serious a violation as the estimated coefficients are not influenced by the presence of serial correlation, but normal standard errors may be biased without further correction. A typical solution to serial dependence -inclusion of lagged dependent variables- is not appropriate for Poisson and negative binomial modeling. Although many variations of time-series modeling for count data have been 16

developed in recent years, the literature remains favorable to GLM models with added binary variables to control for level shifts in specific time units (Mares & Moffett, 2019). Such controls when implemented appropriately can remove most -if not all- time dependence, especially if the source of serial correlation is structural in nature (Cameron & Trivedi, 2013). GLM models are also easier to implement with existing software packages and the interpretation of the coefficients is generally straightforward. In our study we control for level shifts by including, binary coded variables time for the day of the week, the day of the month, month and year (Allison, 2009). To examine if this approach adequately ‘scrubs’ the impact of serial dependence from our data we examined how they would fare in traditional ARIMA models. Here we found that the introduction of our binary controls likely substantially absorbs serial dependence as the added introduction reduces the coefficient sizes of lagged variables, in many cases rendering results for the lags non-significant. It is important to remember that traditional OLS models remain inappropriate as crime counts at the daily level rarely exhibit a normal distribution as even the most common crimes do not occur frequently enough to produce a normal distribution.

We additionally examined the differences between various standard error specifications and ran models with standard, robust, bootstrapped, and Newey West standard errors, as we were marginally concerned about the reliability of our robust standard errors to provide unbiased estimates in the presence of serial correlation. The differences between various types of standard errors were trivial, indicating a fair model fit (Angrist & Pishke, 2009; King & Roberts, 2015). Here we report robust standard errors

(White-Huber), as they are appropriate under conditions of unknown heteroscedasticity and equivalent to standard errors under conditions of homoscedasticity and thereby effectively incorporate any remaining impact of serial correlation in the calculation of significance levels. Our conclusions would not substantially change depending on the selection of any set of standard errors (see our online supplementary materials). Combined we believe our results are a fair representation of the empirical relationships that may modify crime changes during MLB game days. Our first set of models (Table 2) display the results of our analysis on various geographical levels. Our second analysis (Table 4) focuses 17

on team rivalry (Cardinals vs. Cubs) and our final analysis (Table 5) examines the differing impact between games played during daytime and nighttime.

Robustness checks and alternative specifications were examined, but results remain virtually identical. First, full models were compared against models without the “Opportunity” variable. Serious differences in the control variables across models would indicate that the primary independent variable interacts with control variables, however, no substantial changes are noted. Second, various model specifications were attempted by dropping respectively: binary climatic variables, all climatic variables, and month/year fixed effects, but results for the game day coefficients do not change appreciably and conclusions about the significance and direction of the relationship remain intact. Additionally, Poisson and traditional ARIMA models were constructed (including lags for day 1 and day 7), and again results are virtually indistinguishable. Our results thus do appear to be substantially similar across model specification hinting at robust findings. We also examined if away games are statistically similar to non- game days and again find no significant differences. Including a binary away games variable in the models yields no gains in the prediction and were dropped from the analysis presented here. In another variation we restricted the analysis to the MLB season, but here too differences are minimal, indicating our model adequately adjusts for seasonality. We also studied the influence of game wins or losses, but again found no statistical differences. Given the number of models we ran, we can not reasonably display full model results in the tables and are limited to showing readers only the game day coefficients, our theoretical coefficients of interest. In the online supplementary files full model results may be viewed and replicated.

The control variables in the current study display relatively consistent results across crime type and geography. Climatic control variables show very stable results, with higher temperatures typically indicating positive associations to crime levels, and greater precipitation levels typically showing negative associations to crime levels. The controls for the home game days of the other two professional sports teams in St. Louis (Blues and Rams) also show similar significant positive associations to crimes 18

(larceny, motor vehicle theft, simple assaults, disorderly and destruction) as observed during MLB games.

Such a result indicates that while the level of impact may vary depending on the type of game, they appear to influence crime levels in similar ways.

Overall, and Spatial Dimensions

-Table 2 about here-

Results from Table 2 detail geographic differences in the relationship between our proxy for the increased crime opportunity created by Cardinals home games and crime levels; variable “Opportunity”.

The coefficients are exponentiated meaning they may be interpreted as the average proportionate change in daily crime counts given a one million increase in attendance minutes (a typical game generates 6.94 million attendance minutes). Readers interested in a more traditional binary coded home game day variable may find those results in the online supplementary materials.

Models 1-3 examine crimes in the stadium area with Downtown models (1) reporting on the immediate stadium area, Near Downtown models (2) report only results for surrounding areas and City

Center models (3) combine all four neighborhoods near the stadium. Results for Part I offenses (including robbery, aggravated assault, burglary, larceny and motor vehicle thefts) are quite intriguing. As expected, larceny, and motor vehicle theft show significant elasticity on home game days depending on attendees and game length. For example, Downtown counts of larcenies increase by 2.79% per million attendee- minutes (equaling a 19.36% increase per average game -assuming a mean of 6.94 million attendee- minutes); Near Downtown larceny counts increase by an average of 1.22% per million attendee-minutes

(or 8.47% per average game); resulting in an overall combined City Center increase of 2.12% per million attendee-minutes (14.71% per game). It must be highlighted that at least two out of every three larcenies in the City Center area are vehicle related, which means that vehicles appear especially targeted. Also, given that larceny is the most voluminous UCR Part I category it is not surprising that the aggregate crime category increases. Consistent with prior studies, burglaries and aggravated assaults, do not 19

increase significantly during Cardinals games at any geographic level. Robbery shows an interesting pattern. Whereas the Downtown area does see a significant increase during Cardinals games, the areas surrounding it, report a non-significant decrease, resulting in an essentially flat trend in the entire City

Center area. The latter findings may suggest that robbers either purposefully concentrate in the

Downtown area, or their activities are disturbed by the additional traffic during game day. Simple assaults, disorderly conduct and vandalism, crimes more likely committed by game attendees, show increases as the number of attendees and game length increases. These results are theoretically not surprising and consistent with geographical analysis of other professional sports teams. When we add the

Downtown neighborhood together with its surrounding neighborhoods (model 3) we see all crime types, except aggravated assaults, burglary and robbery attain a positive sign and statistical significance.

In models (4) and (5) we examine the degree to which attendance and length of MLB games may be able to influence citywide crime levels. Overall city levels of crime (models 4) increase significantly for many crime types, except robbery, disorderly conduct, aggravated assaults and burglaries. Larcenies, for example, experience an average .31% increase per million attendee-minutes (or a 2.15% average increase per typical game). In the last models (5) the crimes occurring in the City Center are deducted from City wide crime counts to examine if the elasticity of attendance and game length extends beyond the larger stadium area. Here, almost all coefficients are non-significant, except robberies, which turn borderline negative, and vandalism, which remains positive. The latter results are intriguing. They suggest that the weak positive relationship between Downtown robberies and Cardinals games is offset by a citywide decline in robberies on game days. The significant positive association for vandalism in model

(5) is more puzzling as it indicates the City Center area is not the only area in which such crimes increase during Cardinals games. It is important to point out that vandalism is not a ‘clean’ crime category.

Vandalism/criminal damage can include a variety of behaviors, including smashed car windows, brawls with property damage and graffiti. We suspect this finding may be an outcome of people watching the game in local bars throughout the city, something we could not adequately model. In supplementary 20

analysis, we found that these results appear to be stronger and more significant as stadium attendance increases. Perhaps popular matches drive such results. As stadium capacity is reached some fans may watch the game in local neighborhood bars instead of Busch stadium.

In conclusion, we believe the City Center (models 3) area adequately captures the overall localized impact of MLB games in St. Louis. The City Center area is spatially similar to areas in which prior studies have found game days impacts (Billing and Depken, 2012; Breetzke & Cohen, 2013).

Additionally, our current results indicate that once we deduct City Center crimes from the rest of the city, results turn largely non-significant. We suspect that some of the lingering positive values in the last column in Table 2, such as larceny and motor vehicle, may be related to Cardinals games, but given that they occur well away from the stadium, they are not demonstrably driven by stadium attendance directly.

Using the City Center area as our catchment area, we thus argue that the influence of MLB games in the immediate stadium area likely increases larcenies by 2.12% per million attendee minutes (or an averaged crime increase of about 15% per game when multiplied by the average game day opportunity proxy value of 6.94), motor vehicle thefts by about 2.03% per million attendee minutes (or about 14% per averaged game), simple assaults by 2.34% (16% per averaged game), vandalism by 2.10% (15% per averaged game) and disorderly conduct by 3.35% (about 23% per averaged game). Tabulated per game at the city level, the increases driven by the City Center are respectively about 1.93% (larceny), .85%(motor vehicle theft), 1.36% (simple assault), 1.05% (vandalism), and 2.57% (disorderly conduct). Despite a few oddities we thus find the City Center models (models 3) are the most apt geographical scale to measure the likely crime influence of Cardinals games. All following analysis presented here therefore use this scale to measure additional aspects of MLB games in St. Louis, but some additional scales may be found in the supplementary materials.

In Table 3 we tabulate, using City Center as our focus, the added annual crime increases. For crime categories that achieved significance at this geographic level, the annual predicted crime increases during Cardinals games (assuming 81 home games) is 156, of which 107 are Part I offenses. Larcenies, by 21

far, contribute most to the increase, with 92 additional crimes linked to Cardinals games. The total cost associated for all crime increases ranges between $750,000 and $1.2 Million, depending on the preferred method of crime-cost computation. Bottom up estimates reflect the direct accountable cost of crimes such as property damage, hospitalization, court, police and incarceration cost; willingness-to-pay estimates are based on how much surveyed individuals are willing to contribute to achieve as specific crime reduction

(Cohen et al., 2004). The majority of crime increases during MLB games are likely driven, not by stadium attendees, but by offenders exploiting the added opportunities that increased numbers of attendees present. A citywide calculation of crime increases may be found in the Supplementary Materials.

-Table 3 about here-

Rivalry

The St. Louis Cardinals and Chicago Cubs are longtime rivals in MLB and play each other regularly.

Their rivalry has historical roots, and even inspired a special museum exhibit (O’Brien, 2017). During the study period, the Cubs played 180 games in St. Louis, providing a solid sample size to compare the crime relationships between Cubs games and other teams. The percentage of home wins for the Cardinals does not substantially differ when they play the Cubs or other teams (61% vs. 59%). Here we examine if this rivalry may translate into crime differences after accounting for attendance and game length. Table 4 shows the results of two models for each dependent variable. Here we include only variables that achieved some degree of significance in prior modeling (i.e., larceny, robbery, motor vehicle thefts, simple assaults, vandalism and disorderly conduct). To measure team rivalry, we split our opportunity proxy into two separate terms. One term measuring the attendees and game length during Cubs games, and one term measuring the opportunity index during all other home games. This approach allows us to estimate each possibility at the same time against non-game days. Because such an approach shows the coefficient strength and statistical difference from non-game days we ran additional models in which one 22

of the two coefficients was replaced with the overall opportunity-proxy variable to estimate if the effect between Cubs and non-Cubs games is significantly different (significant differences are noted in Table 4).

To illustrate the added value of our opportunity-proxy model, we also include the models with

‘traditional’ binary variables.

Binary game day models show statistically significant differences in larcenies, simple assaults and disorderly conduct between Cubs and non-Cubs games. Once we use variable “Opportunity”, however, the statistical differences virtually disappear, suggesting that the Cubs games produce higher crime levels because they attract more spectators (as game length is roughly equivalent). Disorderly conduct is the only offense that remains significantly higher during Cubs games. Our differential findings based on model choice (traditional binary vs. “Opportunity”) are quite important as they demonstrate that failing to account for attendance and game length may lead to false conclusions about game differences.

We may thus conclude that despite the long-standing team rivalry between the Cardinals and Cubs, fans in St. Louis do not appear to get swept up in negative fan emotions.

-Table 4 about here-

Day/Night Games

Crime opportunities may also be modified by the time of day a game is played. It stands to reason that some criminal acts are easier to accomplish under the cover of night, especially pecuniary offenses. We followed a similar approach to the team rivalry approach and introduce two variables that approximate the differing opportunity structure during day games and night games separately. Again, we present results for a binary approach and an opportunity-proxy approach. Table 5 shows that the model choices now produces substantially similar results. Of the individual crime categories, only larcenies show a significantly larger average increase during night games, which explains the significant uptick in Part I crimes. What this practically means is that if the Cardinals would only play night games, the Opportunity- proxy models predict 110 additional larcenies per year, but if the Cardinals only play day games, the 23

models predict only 62 additional larcenies per year. Given that larcenies are the most common crime associated with increases during Cardinals games this finding has practical implications we discuss below. Aggressive fan behavior does not appear to be influenced by the time of day a game is played.

-Table 5 about here-

In sum, there are strong and statistically significant relationships between crime levels and

Cardinals’ home play days in St. Louis. We further find that this relationship is elastic depending on attendance and game length, suggesting that home game days are not a simple binary event, but provide a varying opportunity structure. Crime increases are primarily predicted around the immediate stadium area. Serious Part I crimes impacted are especially larcenies and motor vehicle thefts. In the City Center larcenies are primarily thefts from parked cars, suggesting that the primary impact of Cardinals games is centered on unguarded vehicles. Importantly, we find no substantive evidence that other crimes, not typically associated with professional sports increase (such as aggravated assaults and burglary).

Consistent with research on other professional sports, several less serious aggressive offenses also see substantial upticks during game days. Team rivalry between the Cardinals and the Cubs may enhance disorderly behavior near the stadium area but does not appear to increase other crimes once attendee and game length numbers are entered into the model. During nighttime games larcenies increase substantially, but we find little evidence that aggressive fan behavior increases.

Theoretically our results provide support for a RAT approach as we find elasticity in crime increases depending on changes in attendees and game length. Additionally, our results show that the strongest increases in crime occur in the area near the stadium. Especially the strong increases in larceny and motor vehicle thefts and the lack of crime increases in burglary speak to the predictive potential of the opportunity model. Our results mirror those found among other sports types and cities, although they appear to be somewhat smaller at the city level (typically about a 2% predicted increase for the affect crime types). Robberies present a less clear picture than expected. Although some increases are detected for this offense in the Downtown area, this signal disappears at the City Center level and turns negative at 24

the City level. There may be several explanations beyond the fact that robbery is a relatively rare crime.

Game days may keep potential victims and offenders inside because they may watch the game on television, which could explain the dip in robberies outside of the City Center area. Additionally, the increased mobility in the City Center area may make it more difficult for offenders to find a target as the larger number of pedestrians and cars act as suitable guardians.

Once we account for the number of attendees and game length we find little evidence to suggest team rivalry makes much difference in generating crime. The only crime for which we do find significant increases during Cubs games in St. Louis is disorderly conduct. Given that disorderly conduct is most often associated with drunken misbehavior, one may at best claim that team rivalry in MLB games perhaps increases alcohol intake, but it certainly does not appear to have a major impact on fights or destructive behavior.

In general, we are especially surprised at the small predicted increases in aggressive behavior

(just shy of 50 per average year). Considering nearly 3 million people attend Cardinals games each year baseball fans appear to be -on the whole- a fairly well-behaved crowd, not likely to be swept up in negative emotions.

Discussion

One may think of several limitations presented by the current study. Our greatest concern relates to the limitations of our city-wide results. While our study shows that the results from the City Center area are largely mirrored in city crime levels, we believe that these results are not always easily replicated in different contexts. St. Louis city, a medium sized city contains a relatively small fraction of the entire metropolitan area (about 10%). Replicating this study in a much larger urban or metropolitan area would likely reduce game effects to such small levels it may resemble statistical noise.

St. Louis hosts numerous other events downtown that may prompt increases in crime but finding a list for such a long period is virtually impossible, and we have to assume that such events occur 25

randomly on both game days and non-game days. We did incorporate controls for other professional sports teams that played during this period (the St. Louis Rams and Blues). Interestingly the results of these other sports teams largely mirror our findings for the Cardinals (see supplementary documentation), further giving evidence that sports games likely only impact certain crimes.

While we provide specific coefficients of changes in crime levels, these percentages have to be seen in the context in which they are produced and are unlikely to fully translate to other settings. Our study shows the strongest predicted game day relationship to larcenies. We believe that other locations in the US should mirror such changes (albeit at different levels). The neighborhoods around Busch stadium are mostly commercial areas with few residential neighborhoods. This is in contrast, for example, to the area around Wrigley Field in Chicago, which is a predominantly residential area with few office buildings. The type of neighborhoods in which stadiums are located will likely have an important impact on base crime levels and the types of crimes most prominent. Therefore, we find it difficult to assess whether and how the proportionate increases in crime found in St. Louis will translate to other places. We suspect that given the diversity of stadium locations in the US, focusing on the relative crime increases may in fact be somewhat moot and rather would argue that the direction and significance of any relationships are more important markers when comparing locations. By extension we see substantial variation in the significance and strength of citywide coefficients in the literature on Football; we expect that additional studies focusing on MLB would reinforce this view. MLB cities vary much in terms of total population, economic productivity and other factors. In effect, city comparisons are often apples to oranges in which contextual differences must be carefully considered. Prior studies indicate the scale at which one measures game day effect may matter (e.g., Billings & Depken, 2012; Breetzke & Cohn,

2013). We encourage future researchers to carefully consider the geographic scale at which measurement takes place.

It is also important to note that our results are limited to those incident that came to the attention of police; many crimes are not reported. The size of underreporting is unknown and there is no way to 26

estimate how this may impact our analysis. Presumably many MLB game attendees are people of middle incomes, more likely to report victimization. On the other hand, many attendees do not live in the city of

St. Louis and this unfamiliarity may reduce people’s willingness to report offenses. If there were to be a substantial difference in crime reporting behaviors between non-game days and games days some of our results could be invalid; unfortunately, there is no way to assess this issue.

Our results indicate that Cardinals games likely generate between $750,000 and $1.2 Million in annual economic damage related to crime. This amount is higher than the $688,000 calculated by Kalist and Lee (2014, p. 14) for NFL games, but one must take into considerations that MLB teams play substantially more games. If one estimates the cost purely based on game attendees (3 million) during a typical year, the average crime cost is between 25 and 40 cents per attendee, a small amount for a team that is worth $1.8 billion and generates $84 per attendee (Forbes, 2017). We believe our calculated costs are a reasonable estimate, but one must also consider that most of the additional cost of crime is not carried by the Cardinals, nor the city of St. Louis. Many victims come from different municipalities surrounding St. Louis. Incarceration costs are typically carried by the state, and insured losses are often transferred onto the entire pool of policy holders.

Nonetheless, we suspect that the greatest cost is not easily monetized. St. Louis has long struggled with the image of being a hotbed for crime and is consistently at the top of the list in crime rankings, flawed as these may be (see Sauter, Stebbins, & Frohlich, 2015). Spectators who are victimized and who live outside the city limits are unlikely to shake such an image.

Conclusion

Results of nearly 23 years of daily data from St. Louis indicate that during Cardinals games many crimes in the larger stadium (up to 1.5 miles) area experience substantial increases (typically around 14-16% per game). This is particularly the case for larcenies, motor vehicle thefts, simple assaults, disorderly conduct 27

and destruction of property. Not surprisingly results show proportionally large increases in the immediate stadium area, but given their overall magnitude, increases also occur at the city level. Based on the increases in the immediate stadium area, annual costs for the most serious offenses are estimated between

$750,000 and $1,200,000 per year. In addition to game day impacts, this study also examines the likely influence of team rivalry and the time of day a game is played (day/night). Whereas only disorderly conduct shows a significant increase during games against the Cardinals’ key rival, larcenies increase significantly when the Cardinals play at night.

Should our specific results hold in different locations, we can give several policy recommendations for law enforcement agencies. First, policing MLB games should focus primarily on crowd control in the immediate stadium area. Second, we recommend a substantive focus on parked vehicles; vehicles appear to be at especially high risk during games. Awareness campaigns that alert game attendees to park vehicles in well-supervised areas and to store valuables out of sight may decrease the desirability of vehicles. A focus on vehicles may be especially important during games played at night.

Future research should examine if our results hold in different locations. We suspect that the proportionate increases we find may be different in other locations, but we feel that in locations surrounding stadiums, pecuniary and some aggressive crimes likely see increases during games days. Our study also shows that failing to incorporate attendance and game length may lead to false conclusions about team rivalry, and we would encourage future studies to incorporate such a measure. Our study accounts for attendance and game length by creating an opportunity-proxy to measure the link of MLB games to crime. Such an approach allows one to standardize the relative impact of various (sports) events and this method may have value beyond our study. Our results show that crime is elastic depending on the duration and attendees to MLB events. We feel that such an approach would be beneficial to policy makers as they seek to weigh the relative benefits and costs of hosting various events.

Notes 28

1. The authors would like to thank Richard Rosenfeld and the anonymous reviewers for suggesting incorporating opportunity differences in our models. 29

References

Allison, P. D. (2009). Fixed effects regression models. Thousand Oaks: Sage. Baumann, R., Ciavarra, T., Englehardt, B., & Matheson, V. A. (2012). Sport franchises, events and city livability: An examination of spectator sports and crime rates. The Economic and Labour Relations Review, 23, 83-98. Billings, S. B. & Depken II, C. A. (2011). Sport events and criminal activity: A spatial analysis. In: R.T. Jewell (ed.) Violence and Aggression in Sporting Contests, 175-187. Springer: New York. Boden, J. M., Fergusson, D. M., & Horwood, L. J. (2013) Alcohol misuse and criminal offending. Drug and Alcohol Dependence, 128, 30-36. Breetzke, G., & Cohn, E. G. (2013). Sporting events and the spatial patterning of crime in South Africa. Canadian Journal of Criminology and Criminal Justice, 55, 387-420. Bursik, R. J., Grasmick, H. G. (1993). Neighborhoods and crime. Lanham: Lexington Books. Cameron, A. C., & Trivedi, P. K. (2013). Regression analysis of count data (2nd ed.). Cambridge: Cambridge University Press. Campaniello, N. (2011) Mega events in sports and crime: Evidence from the 1990 football World Cup. Journal of Sports Economics, 14, 148-170. Caple, J. (2013). Extra innings? Give me more! http://www.espn.com/mlb/story/_/id/9375790/length- mlb-games-not-issue, Accessed 8-1-2017. Carbone-Lopez, K., & Lauritsen, J. (2013). Seasonal variation in violent victimization. Journal of Quantitative Criminology, 29, 399-422. Card, D., & Dahl, G. B. (2011). Family violence and football: The effect of unexpected emotional cues on violent behavior. Quantitative Journal of Economy, 126, 103-143. Cohen, L., & Felson, M. (1979). Social change and crime rate trends: a routine activity approach. American Sociological Review, 44,588-608. Cohen, M., Rust, R. T., Steen, S., Tidd, S. T. (2004). Willingness-to-pay for crime control program. Criminology, 42, 89-110.Cohen, M., Piquero, A.R., & Jennings, W. G. (2010). Studying the cost of crime across offender trajectories. Criminology and Public Policy, 9, 279-305. Cohn, E. G., & Rotton, J. (2003). Even criminals take a holiday. Journal of Criminal Justice, 31, 351- 360. Forbes (2017). The business of baseball: 2017 ranking. https://www.forbes.com/teams/st-louis-cardinals/, accessed 10-1-17. Humphreys, B. R. & Pyun H. (2018) Professional sports and traffic congestion: Evidence from US cities. Journal of Regional Science, https://doi.org/10.1111/jors.12389 Kalist, D. E., & Lee, D. Y. (2014) The National Football League: Does crime increase on game day? Journal of Sports Economics. 17, 863-882. King, G., Keohane, R. O., & Verba, S. (1994). Designing social inquiry: Scientific inference in Qualitative research. Princeton: Princeton University Press. 30

King G., Roberts, M. E. (2015). How robust standard errors expose methodological problems they do not fix, and what to do about it. Political Analysis, 23, 159-179. Larrick, R. P., Timmerman, T. A., Carton, A. M., & Abrevaya, J. (2011) Temper, temperature and temptation: Heat-related retaliation in baseball. Psychological Science, 22, 423-428. Lebeau, J. L., & Corcoran, W. T. (1994). Changes in calls for service with changes in routine activities and the arrival and passage of weather fronts. Journal of Quantitative Criminology, 6, 269-291. Lebeau, J.L., & Langworthy, R. (1986). The linkages between routine activities, weather and calls for police services. Journal of Police Science Administration, 14, 137-145. Levitt, S. D. (1996). The effect of prison population size on crime rates. The Quarterly Journal of Economics, 111, 319-351. Mares, D. (2013). Climate change and levels of violence in socially disadvantaged neighborhood groups. Journal of Urban Health, 90, 768-783. Mares, D., Moffett, K. (2016). Climate change and interpersonal violence: a global estimate and regional inequities. Climatic Change, 135, 297-310. Mares, D., Moffett, K. (2019). Climate change and crime revisited: An exploration of monthly temperature anomalies and UCR crime data. Environment and Behavior, forthcoming. Marie, O. (2016). Police and thieves in the stadium: measuring the (multiple) effects of football matches on crime. Journal of the Royal Statistical Society: Series A, 179, 273-292. Munyo, I., & Rossi, M. A. (2013). Frustration, euphoria, and violent crime. Journal of Economic Behavior & Organization, 89, 136-142. O’Brien, D. (2017). Lincoln Museum to put Cardinals-Cubs rivalry in spotlight, Belleville News Democrat, February 20th, 2017. http://www.bnd.com/news/local/article133835589.html Accessed 8-28- 17. Ostrowsky, M. R. (2014). The social psychology of alcohol use and violent behavior among sports spectators. Aggression and Violent Behavior, 19, 303-310. Pyun, H., & Hall, J. C. (2016). Does the presence of professional football cause crime in a city? Evidence from Pontiac Michigan. West Virginia University: Department of Economics Working Paper Series No. 16-02. Pyun, H. (2018). Exploring causal relationship between Major League Baseball games and crime: A synthetic control analysis. Empirical Economics, DOI: doi.org/10.1007/s00181-018-1440-9. Reed, J. (2017). MLB history: Looking back at MLB teams that relocated. FoxSports, June 30th. http://www.foxsports.com/mlb/story/mlb-history-looking-back-at-mlb-teams-that-relocated-012017 Accessed 8-30-17. Rees, D. I., & Schnepel, K. T. (2009). College football games and crime. Journal of Sports Economics, 10, 68-87. Roberts, J. V., & Benjamin, C. J. (2000). Spectator violence in sports: A North American perspective. European Journal on Criminal Policy and Research, 8, 163-181. Rotton, J., & Cohn, E. G. (2000). Violence is a curvilinear function of temperature in Dallas. Journal of Personality and Social Psychology, 78, 1074-1081. 31

Sauter,M. B., Stebbins, S., & Frohlich, T. C., (2016). The most dangerous cities in America. USA Today, October 1st, 2016. https://www.usatoday.com/story/money/business/2016/10/01/most-dangerous-cities- america/91227778/ Accessed 8-31-2017. Seff, M. (2015). 20 Reasons Why Baseball is Better Than Football. Draft America, November 17th, 2015. http://draftamerica.com/20-reasons-why-baseball-is-better-than-football/ Accessed 8-31-2017. Vermillion, M., Stoldt, G. C., & Bass, J. (2009) Social problems in Major League Baseball: Revisiting and expanding Talamini’s analysis twenty years later. Journal of Sport Administration and Supervision, 1, 23-38. Ward, E. R. (2002). Fan violence. Social problem or moral panic? Aggression and Violent Behavior, 7, 453-475. Wolfe, J., Martinez, R., & Scott, W. A. (1998). Baseball and beer: An analysis of alcohol consumption patterns among male spectators of major league sporting events. Annals of Emergency Medicine, 31, 629- 632. Ya, Y., Mckinney, C. N., Caudill, S. B., & Mixon, F. G. (2016). Athletic contests and individual robberies: an analysis based on hourly crime data. Applied Economics, 48, 723-730.

32

Tables

Table 1. Select Non-Game Day Game Day Descriptive Statistics- (n=6,444) (n=1,773) Daily means and standard deviations1

Mean Standard Deviation Mean Standard Deviation Robbery 0.268622 0.54703 0.334461 0.60035 Aggravated Assault 0.238206 0.749293 0.291596 0.695943 Burglary 0.296865 0.598915 0.350254 0.633656 Larceny 4.129423 2.947891 5.216018 3.197793 Motor Vehicle Theft 0.485878 0.859501 0.654822 0.991143 Vandalism 0.701583 0.99472 0.915962 1.136427 Disorderly Conduct 0.240224 0.535288 0.478849 0.788767 Simple Assault 0.58473 1.051428 0.888325 1.360912 All Part I 5.43622 3.569291 6.870276 3.896239 Max. Temperature (F) 62.5126 20.11088 81.79543 10.86818 Precipitation in Inches 0.1130014 0.3306119 0.1075361 0.3110928 Snow on Ground in Inches 0.1799086 0.8498886 0 0 Snow Fall in Inches 0.0615417 0.4574696 0.0000666 0.002805 Blues play home % 13.40782 3.32769 Rams play home % 2.40534 1.01523 1. Crime data means and standard deviations are for stadium area (Downtown St. Louis) only. Statistics for additional geographic levels may be found in the Supplementary files

33

Table 2. Geographic and Crime Differences, Opportunity-Proxy Game Day Coefficients Only 1

(1) (2) (3) (4) (5) Dependent Variable Downtown Near Downtown City Center City City – City Center

All Part I Crimes 1.0259** 1.0094** 1.0181** 1.0022** 1.0007 (0.002360) (0.002346) (0.001795) (5.630e-04) (5.716e-04) Robbery 1.0194* 0.9938 1.0051 0.9976 0.9968+ (0.008144) (0.007069) (0.005441) (0.001711) (0.001811) Agg. Assault 1.0047 1.0054 1.0047 1.0003 1.0000 (0.01089) (0.008279) (0.006746) (0.001674) (0.001716) Burglary 1.0108 0.9993 1.0051 1.0016 1.0015 (0.007467) (0.007099) (0.005368) (0.001101) (0.001115) Larceny 1.0279** 1.0122** 1.0212** 1.0031** 1.0006 (0.002575) (0.002815) (0.002023) (7.738e-04) (7.993e-04) MVT 1.0314** 1.0120* 1.0203** 1.0028* 1.0016 (0.006832) (0.005781) (0.004524) (0.001383) (0.001423) Simple Assault 1.0407** 1.0073 1.0234** 1.0027* 1.0006 (0.006630) (0.005859) (0.004463) (0.001275) (0.001327) Vandalism 1.0332** 1.0110* 1.0210** 1.0038** 1.0024* (0.005382) (0.004692) (0.003688) (0.001143) (0.001191) Disorderly 1.0709** 0.9884 1.0335** 1.0031 0.9987 (0.007814) (0.008104) (0.005616) (0.001940) (0.002058)

1. Results only display coefficients for opportunity-proxy game-day coefficients. Opportunity-adjusted coefficients may be read as ~proportion change per 1,000,000 additional attendee-minutes. Control variables could not reasonably be fitted in the tables but are available in the Supplementary files. Robust Standard Errors in parentheses; ** p<.01, * p<.05, + p<.1

34

Table 3. Estimated Yearly Crime and Cost Increases During Game Days, City Center Area

Average daily Percent increase Additional “Bottom “Willingness to Pay” crimes per game crimes (annual) Up” estimate estimate

All Part I 11.04 12.56 112 Larceny 7.72 14.71 92 $309,000.73 $441,429.61 MVT 1.29 14.09 15 $158,919.27 $300,180.85 Simple Assault 1.47 16.24 19 $255,137.43 $440,691.93 Vandalism 1.61 14.57 19 $22,797.83 $45,595.66 Disorderly Conduct 0.57 23.25 11 $6,437.81 $12,875.61

Totals $752,293.07 $1,240,773.66

Tabulated from Cohen, Piquero & Jennings (2010).

Crime costs adjusted for inflation using Bureau of Labor Statistics inflation calculator. https://data.bls.gov/cgi-bin/cpicalc.pl 35

Table 4. Team Rivalry- Cardinals vs. Cubs, Game Day Coefficients Only All Part I Robbery Larceny MVT Simple Vandalism Disorderly Crimes Assault

Opportunity-Proxy Models

Cubs Play 1.0223** 1.0045 1.0282** 1.0185+ 1.0362** 1.0293** 1.0694**b (0.004208) (0.01235) (0.004842) (0.01037) (0.009834) (0.007821) (0.01095) Other Teams 1.0175** 1.0052 1.0201** 1.0205** 1.0214** 1.0196** 1.0267**b (0.001867) (0.005682) (0.002102) (0.004714) (0.004653) (0.003870) (0.005874)

Binary Game Day Models

Cubs Play 1.2044**b 1.0563 1.2607**b 1.1634+ 1.3412**a 1.2478** 1.7041**b (0.03792) (0.1047) (0.04542) (0.09447) (0.09974) (0.07713) (0.1469) Other Teams 1.1286** b 1.0265 1.1488**b 1.1635** 1.1575**a 1.1374** 1.2091**b (0.01455) (0.04132) (0.01686) (0.03856) (0.03862) (0.03149) (0.05099)

Robust Standard Errors in parentheses, ** p<.01, * p<.05, + p<.1 a. Indicates difference between Cubs Game and Other Teams is significant p<.1 b. Indicates difference between Cubs Game and Other Teams is significant p<.01 Because of differences in scale Opportunity-Proxy and Binary models’ coefficients cannot be compared directly. Opportunity- Adjusted coefficients may be read as ~proportion change per average 1,000,000 additional attendee-minutes and binary coefficients may be interpreted as ~ proportion change during average Cardinals home game.

36

Table 5. Time of Day- Night Games vs. Day Games, Game Day Coefficients Only Time of Day All Part I Robbery Larceny MVT Simple Vandalism Disorderly Crimes Assault

Opportunity-Proxy Models

Night Game 1.0214**b 1.0092 1.0254**b 1.0218** 1.0235** 1.0214** 1.0383** (0.002083) (0.006519) (0.002379) (0.005189) (0.005397) (0.004466) (0.006733) Day Game 1.0129**b 0.9989 1.0144**b 1.0180** 1.0233** 1.0203** 1.0263** (0.002651) (0.007701) (0.002971) (0.006746) (0.006126) (0.005121) (0.007771)

Binary Game Day Models

Night Game 1.1582** b 1.0630 1.1883**b 1.1679** 1.1663** 1.1389** 1.2872** (0.01637) (0.04844) (0.01926) (0.04222) (0.04403) (0.03563) (0.06019) Day Game 1.0947** b 0.9684 1.1067**b 1.1549** 1.1934** 1.1670** 1.2131** (0.02129) (0.05577) (0.02450) (0.05749) (0.05510) (0.04539) (0.07266)

Robust Standard Errors in parentheses, ** p<.01, * p<.05, + p<.1 a. Indicates difference between Night Game and Day Game is significant p<.1 b. Indicates difference between Night Game and Day Game is significant p<.01 Because of differences in scale Opportunity-Proxy and Binary models’ coefficients cannot be compared directly. Opportunity- Adjusted coefficients may be read as ~proportion change per average 1,000,000 additional attendee-minutes and binary coefficients may be interpreted as ~ proportion change during average Cardinals home game.

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