Emotional Judges and Unlucky Juveniles
Ozkan Eren
Louisiana State University
Naci Mocany
Louisiana State University and NBER
Preliminary Draft-Please Do not Circulate
Abstract
In this paper, we test the predictions of general class of expectation-based reference point models by
exploiting a natural experiment. We analyze the behavior of a highly educated group of professionals,
the behavior of whom should, by law, be free of person-speci…c reference points. Speci…cally, we examine
the e¤ects of emotional shocks associated with the unexpected outcomes of games played by a prominent
college football team on judicial decisions handed down by judges. We …nd that unexpected losses increase
disposition length on juvenile defendants imposed by the judges by around 6.4 percent. Unexpected wins,
on the other hand, have no e¤ect on judges’ decisions. Similarly, losses when the game was expected
to be close have no signi…cant impact. Our results also show that the e¤ects of emotional shocks on
disposition length stemming from unexpected losses are larger for minorities suggesting evidence for
treatment disparity. Finally, we implement a similar analysis using the game outcomes for the local
professional football team and …nd no e¤ects from unexpected losses or wins.
JEL: D84; J00; K42; Z20.
Keywords: Disposition Length; Point Spread; Randomness; Reference-Based Preferences and Treatment
Disparity.
E. J. Ourso College of Business, Department of Economics, Louisiana State University, Baton Rouge, LA 70803. Tel.
(225)-578-7193. Fax. (225)-578-3807. Email: [email protected]
y
E. J. Ourso College of Business, Department of Economics, Louisiana State University, Baton Rouge, LA 70803. Tel.
(225)-578-4570. Fax. (225)-578-3807. Email: [email protected]
1
Introduction
Theories of expectation-based, reference dependent preferences postulate that economic agents assess the
outcome of a choice by its departure from a reference point which is determined by the probabilistic
beliefs held in the past about the outcome (Kahneman and Tversky 1979; Koszegi and Rabin 2006).
Despite their intuitive appeal, models of expectation-based reference point formation is di¢ cult to test,
and empirical evidence is predominantly limited to laboratory experiments (Abeler et al. 2011; Gill and
Prowse 2012; Banerji and Gupta 2014). These experiments provide important insights, however, it is
unclear whether …ndings from a laboratory environment can be generalized to real life (Levitt and List
2009). Thus, researchers are increasingly trying …nd ways to test the relevance of models of referencebased preferences in settings outside the lab (Crawford and Meng 2011; Card and Dahl 2011; Pope
and Schweitzer 2011; Hossain and List 2012). Existing evidence is generally obtained from small scale
experiments involving agents with lower socioeconomic status (e.g., blue-collar workers, cab drivers and
domestic abusers).
In this paper, we test the predictions of general class of expectation-based reference point models by
exploiting a natural experiment. We analyze the behavior of a highly-educated group of professionals, the
behavior of whom should, by law, be free of person-speci…c reference points. Speci…cally, we examine the
e¤ects of emotional shocks associated with the unexpected outcomes of games played by a prominent local
college football team— Louisiana State University (LSU)— on judicial decisions handed down by judges.
To the best of our knowledge, this is the …rst and by far the largest experiment testing for reference point
formation and loss aversion by targeting the universe of agents from the top of the socioeconomic status
distribution.
Borrowing the identi…cation strategy developed in Card and Dahl (2011), we use information contained
in the betting markets to determine the reference point for gain-loss utility (Koszegi and Rabin 2006).
More precisely, we use the pregame point spread as fans’(judges in our case) rational expectations about
the outcome of the game. To the extent that pregame point spread provides e¢ cient prediction of game
1
outcomes, controlling for the spread allows us to interpret any di¤erential impact between a win and a
loss as the causal impact of the game outcome. Using this identi…cation strategy and county-level crime
data, Card and Dahl (2011) …nd that unexpected losses by a National Football League (NFL) team lead
to an increase in domestic violence against females in cities and counties that are home to that NFL
team, while unexpected wins have no impact.
A key ingredient of our analysis is the fact that LSU football team, with its long and successful
history in college football, has an extremely strong and loyal followers. The fan base of the team goes
well beyond the student body of the university. For example, average attendance to home games was
around 87,000 fans in 2000, meaning that on a typical night in the LSU football stadium there were
more people in attendance than the population of the majority of the parishes (counties) in the state
(Scott 2014). In these games, students only constituted less than 18 percent of the total attendees and
around 43 percent of attendees were from outside the Baton Rouge Metropolitan Statistical Area (home
of LSU). Describing LSU football as an event would be a huge understatement for residents of the state
of Louisiana. It is deeply ingrained into the culture of the state. People schedule their wedding dates
based on LSU games, convention halls and similar organizations are besieged by phone calls the moment
LSU schedule for the following year is …nalized and charitable organizations have their fund-raiser events
on the nongame weeks (Feinswog 2013).
By special permission from the Louisiana Department of Public Safety and Corrections, Youth Services, O¢ ce of Juvenile Justice, we obtain access to the universe of defendant …les from 1996 to 2012. For
each …le, we have basic demographic information on the defendants, details of the o¤ense committed, as
well as information on the disposition (sentence) length and disposition type (i.e., custody or probation).
The …les also contain the name of the judge who adjudicated the case. We linked our defendant data
to LSU college football team records over a period of more than 15 years and use this linked data (after
having merged with judge attributes) to test the predictions of expectation-based reference point behavior
formation.
2
Our ability to employ micro-level data also allows us to make inference on two other important
dimensions. First, if the market assessment of the outcome of a game is fully contained in the value of
the pregame point spread, one would expect the estimates of the e¤ects of emotional shocks on judges’
disposition decisions to be insensitive to the inclusion of juvenile/judge characteristics (e.g., judge …xed
e¤ects) as long as the spread is controlled for in the model estimations. Thus, using our rich data, we
can further test the validity of the e¢ cient prediction hypothesis. Next, heterogeneity analysis based on
observable juvenile characteristics provides important information pertaining to treatment disparity and
shed light on the equal protection clause of the law.
Our results provide four key insights. First, we …nd that upset losses (i.e., LSU losses when they were
expected to win by four points or more) increase disposition length on juvenile defendants imposed by the
judges by around 6.4 percent, relative to the average disposition length. It also appears that the e¤ect of
an upset loss on judicial decisions persist throughout the week following a Saturday game. In contrast,
close losses or upset wins (i.e., LSU wins when they were expected to lose by four points or more) have no
signi…cant impact on the disposition length set by the judges. These results taken together provide credible evidence on the expectation-based reference point behavior formation/loss aversion among juvenile
court judges, at least in the state of Louisiana. Second, consistent with the e¢ cient prediction hypothesis
of pregame point spread on game outcomes, controlling for an extensive set of juvenile and judge characteristics has no impact on the estimated e¤ects of emotional shocks experienced by the judges. Third,
it appears that the emotional cuing e¤ects are substantially more pronounced among judges in parishes
closer to home of LSU, for juvenile defendants with severe o¤enses and at more salient games. Finally,
our further analysis by subgroups based on juvenile characteristics suggest that the e¤ects of emotional
shocks on disposition length stemming from unexpected losses are larger for black youth. Thus, courtroom outcomes may not be color blind. Several robustness checks supports our conclusions. We also
implement a similar analysis using the game outcomes for the local NFL team (New Orleans Saints) and
…nd no emotional cuing e¤ects on dispositional length from unexpected losses or wins. Lower degree of
3
fan devotion and emotional attachment may explain the divergence in the results.
The remainder of the paper is organized as follows. Section 2 discusses the institutional settings.
Section 3 presents the data. Section 4 describes the econometric methodology. Section 5 presents the
results. Conclusions and policy implications are provided in Section 6.
2
Institutional Settings
In Louisiana, youth through age 17 may enter the juvenile justice system when they are accused of
committing a crime and arrested or referred by the police to a juvenile court.1 Having received a formal
complaint from a local law o¢ cer, the District Attorney’s (DA) O¢ ce must decide whether or not to
petition the case to the court. Prosecutors may choose not to do so because of lack of su¢ cient evidence.
Alternatively, to prevent incarceration, the DA’s O¢ ce may choose to enter into an informal agreement
(diversion program) with the juvenile and the parents which occasionally entails a child to participate
in community service, restitution, or treatment and comply with certain behavioral requirements such
as school attendance (Louisiana Children’s Code CHC 631). Finally, prosecutors may proceed with a
petition. When the case moves to adjudication, the disposition, which is similar to a sentence in the
adult courts, must be determined by a juvenile court judge (Louisiana Children’s Code CHC 650-675).
Under the provisions of the Louisiana juvenile justice system, a computer generated random allotment
(open to public) is implemented on a daily basis by the Clerk’s o¢ ce for all cases …led in each district
court (Rules for Louisiana District Courts, Chapter 14, Appendix 14.0A, various years). Thus, cases are
randomly assigned to judges within each district court.2
Judges may simply dismiss the case if the prosecutor is unable to provide evidence to …nd the youth
delinquent. The child would then be found not guilty and does not enter into the juvenile justice system.
If a youth is found guilty, judges have to make a disposition (i.e., whether to place the juvenile in secure
1
2
Children under age 10 are addressed through the Families in Need of Service programs. The age of majority is 18.
Random assignment of judges to cases excludes charges involving heinous crimes such as …rst-degree murder.
4
custody or probation), and set the disposition length. Judges are responsible for weighing the severity
of the o¤ense committed and the prior o¤ense history of the youth. In general, they shall impose the
least restrictive disposition consistent with the circumstances of the case, the health and safety of the
child, and the best interest of the society (Louisiana Children’s Code CHC 683).3 The judges can set a
maximum duration of disposition up to youth’s 21st birthday.4
3
Data
3.1
Defendant Data and LSU College Football Team Records
The defendant data for this study come from the Louisiana Department of Public Safety and Corrections,
Youth Services, O¢ ce of Juvenile Justice (OJJ) and include all case records from 1996 to 2012 in which
juvenile was found to be delinquent. For each case record, we have information on both the juvenile
defendant and the case itself. Information on defendants include the name, race, gender and age of the
juvenile, parish of residence, parish of o¤ense committed, the exact statute o¤ense committed, the date
the individual was admitted into the juvenile system and a unique individual identi…er. This identi…er
allows us to follow the individual over time and observe concurrent and sequential entrances in the OJJ
system. The case data include information on the date the juvenile was disposed before the judge, the
judge’s disposition type and disposition length, the court in which the disposition was held and the name
of the judge. By using the names of the judges provided in the OJJ administrative data, we also gathered
the following information for each judge: race, gender, political party a¢ liation, and age.5
Our main outcome of interest is the disposition length imposed by the judge. In order to circumvent
any potential confounding e¤ects that may arise from multiple o¤enses and/or criminal history of the
juvenile, we limit our attention to …rst-time delinquents ages 10 through 17 who were convicted for only
3
In setting the appropriate disposition, judges may also consider the predisposition investigation report prepared by
probation o¢ cers involving information about youth, their risk to public safety and their needs (Louisiana Children’s Code
CHC 680).
4
Statutory exclusion laws apply to certain o¤enses to youth over 14 in the state of Louisiana.
5
We collect information on judges from variety of sources (Louisiana District Judges Association Periodicals 1956-2000).
5
one statute o¤ense. We link our defendant data to LSU college football team records over a period of
more than 15 years. Speci…cally, we concentrate on all dispositions handed down by a judge following
a game during the college football season and post season (i.e., bowl games). In an attempt to infer on
the transitory nature of emotional shocks, as well as to observe all weekdays, we limit our sample to only
include weekdays following a Saturday game. Having imposed these restrictions, we ended up with a
sample of 10,671 unique case (juvenile) records from a total of 207 judges.
Table 1 presents the descriptive statistics for juveniles and judges. Panel A displays juvenile attributes
while Panel B presents judge characteristics. The average disposition length in our sample is around 530
days. Figure 1 displays the distribution of disposition length imposed by the judges. There is bunching
around every half-year thresholds (i.e., half a year, one year and one and a half year) with a median of 366
days. The spikes in disposition length are simply driven by the high frequency of a few o¤enses within the
bins (i.e., simple burglary, ungovernable and possession of drugs). The average incarceration rate (secure
and non-secure custody) is 29 percent and this is slightly higher than the national average (25 percent
in 2011) among all adjudicated delinquent cases (Hockenberry and Puzzanchera 2014).6 Consistent with
the state demographics, black and white juveniles comprise more than 98 percent of all defendants and
females constitute 22 percent of the defendant sample. Using the OJJ’s broad crime classi…cation (i.e.,
felony, misdemeanor and others), we also observe that felonies (43 percent) make up a non-negligible
fraction of all o¤enses. The judiciary included in this study is largely white (89 percent), male (77
percent) and they are more likely to be a¢ liated with the Democratic Party with an average age of 56.3.7
Interestingly, in terms of observable characteristics, the judge sample used in this study is similar to that
reported in Abrams et al. (2012) for adult courts in Cook County of the state of Illinois. Note also that
47 percent of the judges graduated from LSU law school.
6
As for non-incarceration disposition options, probation forms the backbone of the Louisiana’s juvenile justice system.
Our de…nition of incarceration status (secure and non-secure custody) is standard in the literature (see, for example, Aizer
and Doyle 2013).
7
In the analysis below, we use the actual age of the judge at the disposition date. For summary statistics, we report the
judge’s age at the last observed disposition date.
6
Table 2 reports LSU win-loss records for college football seasons from 1996 to 2012. There is large
variation from year to year. For example, LSU had a disappointing season with a 3-8 win-loss record in
1998 while LSU had a 8-4 win-loss record in 2000.
3.2
LSU College Football Team’s Predicted and Actual Outcomes
Spread betting on professional/college football games is organized through Las Vegas bookmakers and
the market assessment of the outcome of a game is assumed to be contained in the closing value of the
spread. As such, if the pregame point spread is -5 for LSU against another team, this means that LSU
is predicted to win by 5 points or more. Card and Dahl (2011) provide credible evidence on e¢ cient
prediction of the pregame point spread on game outcomes in the NFL. To build upon this evidence, we
collected data on pregame point spreads and …nal scores of all LSU college football games for seasons
from 1996 to 2012 and ran a simple regression of actual spread on the predicted spread (closing value of
the pregame point spread).8 The coe¢ cient estimate from this exercise is 0.98 (0.07) with a R2 value of
0.49. Figure 2 also plots the relationship between actual and predicted point spread. It is important to
note that the estimated e¤ect on the predicted spread for LSU football games is almost identical to that
reported in Card and Dahl (2011) for all NFL games played during the 1995-2006 seasons. We provide
further evidence on the validity of e¢ ciency assumption using micro-level data in section 5.1.
Having shown some support for e¢ cient prediction hypothesis of the point spread on game outcomes
in college football, our next step is to divide the point spread in an attempt to identify emotional cues.
We de…ne ex ante classi…cation of LSU college football games as (i) predicted win if point spread is -4
or less, (ii) predicted close if point spread is between -4 and 4, and (iii) predicted loss if point spread is
4 or more. Our results are robust to predicted game classi…cations using di¤erent spread value cuto¤s
(discussed in section 5.6).
Our sample includes all dispositions during the weekdays following a Saturday game. This restriction
8
Pregame point spread data come from an online betting agency (www.goldsheet.com) and game statistics are obtained
from LSU athletics department (www.lsusports.net).
7
generates 184 games or 85 percent of all games over 16 years (Panel A, Table 3). LSU football team
won 75 percent of all games played on Saturday (Panel B, Table 3). Of the games played on Saturdays
and those with actual point spread values, 122 (68 percent) were predicted wins, 29 (16 percent) were
predicted close games and 28 (16 percent) were predicted losses.9 LSU (i) lost around 12 percent of the
games they were favored to win by four or more points— upset losses, (ii) lost more than 48 percent of
the predicted close games— close losses, and (iii) won 35 percent of the games they were predicted to lose
by four or more points— upset wins. The total number of dispositions associated with game outcomes
is reported beneath each category in Panel B of Table 3. Figures 3-5 display the frequency distribution
of opponent teams for all Saturday games disaggregated by predicted spreads and actual outcomes of
the games. Unexpected game outcomes generally involve opponent teams that are known to be LSU’s
historical rivals such as the University of Alabama and University of Florida. Finally, LSU college football
team was ranked in the top 10 based on Associated Press rankings for 78 games (44 percent) played on
Saturdays over the sample period.
4
Empirical Methodology
To estimate the impact of emotional cues generated by unexpected wins or losses on disposition length
imposed by the judges, we specify the following equation
Dijdkl =
+
4 1(
0
+
1 1(Sk 1l
4 < Sk
1l
4) +
< 4)1(yk
1l
2 1(Sk 1l
= 0) +
+Xijdkl +
j
+
4)1(yk
5 1(Sk 1l
d
+
k
+
1l
= 0) +
4) +
l
3 1(
6 1(Sk 1l
4 < Sk
4)1(yk
1l
1l
< 4)
= 1)
+ "ijdkl
where Dijdkl is the disposition length for defendant i set by judge j in day d of week k in season l, Sk
9
There were 5 games with no spread betting.
8
(1)
1l
is
the observed pregame point spread for a Saturday game and is de…ned as indicator functions for the three
ranges of the spread value, yk
1l
is another indicator function taking the value of one for LSU football
team victory. Xijdkl represents a set of observed juvenile (i.e., gender, race/ethnicity, age and detailed
o¤ense type), judge (i.e., gender, race/ethnicity, party a¢ liation and age) and game (i.e., home/bowl
game status) characteristics,
j
is the set of judge …xed e¤ects,
d,
k
and
l
denotes day of the week,
week and season e¤ects, respectively and "ijdkl is the error term.
The coe¢ cient estimates for
2,
4
and
6
measure the e¤ects of an upset loss, a close loss and
an upset win on disposition length set by the judges, respectively. In the estimations below, we treat
predicted win (1(Sk
1l
4)) as our base category. As discussed in the robustness section, treating
nongame (bye) weeks as the base category produce very similar results.
The key identifying assumption underlying this framework is that the outcome of a college football
game is as good as random, conditional on pregame point spread. Put di¤erently, to the extent that Las
Vegas spread provides e¢ cient prediction of the LSU college football game outcomes, controlling for the
point spread in equation (1) allows us to tease out the e¤ects of emotional cues of game outcomes. The
standard errors are clustered at the day of the week week season level. The results remain intact if we
instead cluster at the week season or judge level.
5
5.1
Results
Baseline Results
To begin, we estimate a parsimonious model where we only include spread indicators (treating predicted
win as the base category), interactions of spread indicators with win/loss variables and controls for day of
the week, week and season. The results are reported in Table 4. The …rst column shows that an upset loss
leads to a statistically signi…cant 36 days increase in the disposition length set by the judge. Turning to
other coe¢ cient estimates associated with game outcomes (second and third rows), the estimated e¤ect
9
from a close loss (upset win) is positive (negative), but imprecisely estimated. Under the assumption
of college game outcomes conditional on pregame point spread being as good as random, one would
expect the impact of emotional cues on judicial decision generated by unexpected wins and losses to be
insensitive to the inclusion of any control variables. To examine this, we extend our baseline speci…cation
to incrementally add controls for observable judge (Column 2), juvenile (Column 3) and game (Column 4)
characteristics. The coe¢ cient estimates on the e¤ects of upset losses, close losses and upset wins remain
intact. Adding detailed measures of o¤ense types (178 o¤ense …xed e¤ects) and judge …xed e¤ects to our
extended speci…cation from column 4 do not alter our …ndings either. A comparison of our most extensive
speci…cation in the last column of Table 4 with our baseline speci…cation in Column 1, the estimated
e¤ect on an upset loss is very similar in magnitude. Speci…cally, an upset loss increases disposition length
set by the judges by 34 days. Taking the average disposition length (530 days) as our benchmark, this
magnitude corresponds to a 6.4 percent increase. The impact of close losses and upset wins on disposition
length are smaller in magnitude and they are not statistically di¤erent from zero (Column 6, Table 4).
Thus far, we ignored the transitory nature of emotional shocks on judicial decision process and treated
all weekdays equally. There is indeed non-negligible evidence in the psychology literature pointing out
a relatively long lasting (almost over a week) association between emotional shocks and major sporting
events (Phillips 1983; Miller et al. 1991). It is also conceivable that the cuing e¤ect attributable to an
upset loss fades out as judges proceed through the week. To address this potential transitory nature of
emotional cues associated with college football game outcomes, we interact our three measures with an
early week indicator. Table 5 presents the results from this exercise for our most extensive (and preferred)
speci…cation. We treat early week indicator to only include Monday in the …rst column, while Monday
through Wednesday are assumed as early weekdays in the second column of Table 5. The interaction
term for the e¤ect of an upset loss with early week indicator is positive in both columns, however, neither
one of them is statistically signi…cant. The e¤ects for a close loss and an upset win continues to be
imprecisely estimated.
10
We further examine the long-run cuing e¤ects by incorporating spread indicators and interactions of
spread indicators with win/loss variables from the week before (Sk
2l
and yk
2l )
into the speci…cation
from equation (1). Table A1 in the Appendix provides the results after limiting our sample to games
played in only consecutive weeks. Column 1 provides our benchmark regression results for the smaller
sample, column 2 reports the estimates where we replace spread and game outcome measures with those
from the week before and …nally, we control for current and lagged measures simultaneously in the last
column of Table A1. Viewing the set of results from the table, we do not observe any spillover e¤ects of
emotional cues from the week before.10
Overall, our baseline speci…cations provide three important insights. First, even though the coe¢ cient
estimates on the e¤ect of an upset and a close loss are not statistically di¤erent from each other, we detect
a larger and a signi…cant e¤ect from an upset loss on disposition length imposed by the judges. This
suggests empirical support for expectation-based reference point behavior formation in the sense that the
in‡uence of a negative cue is stronger when a loss is less anticipated (Koszegi and Rabin 2006). Second,
our results indicate that judges show stronger emotional reactions to an upset loss than they do to an
upset win (as evidenced from a simple comparison of the estimated e¤ects of upset losses and upset wins).
Asymmetry between negative and positive shocks also lends further support to loss aversion, agents value
losses more than they value commensurate gains (Kahneman and Tversky 1979; Koszegi and Rabin 2006).
Finally, we do not …nd strong evidence for the transitory nature of emotional cues associated with college
football game outcomes on judicial decisions over the week. The e¤ect of an upset loss may persist over
the entire week but emotional cues do not carry over the week after.11
10
Treating nongame weeks (bye weeks) as the omitted category and including full set of interactions between ex ante
classi…cation of games and outcomes of games yield identical results.
11
Using aggregate level county data, Card and Dahl (2011) show that an upset loss leads to around 10 percent increase
in the rate of at-home violence by men against their wives or girlfriends. Closes losses and upset wins, on the other hand,
have little to no impact on domestic violence. The authors also show that violence is concentrated in a narrow time interval
surrounding the end of the game. Comparing our results with Card and Dahl (2011), we …nd similar but more persistent
e¤ects of emotional cues to unexpected game outcomes. Several factors including but not limited to the unit of observation
(judge vs. domestic abuser), outcome of interest (disposition vs. domestic violence) and nature of the data (micro vs.
aggregate) may all contribute to the divergence in the results of these two natural experiments.
11
5.2
Emotional Cues, Spatial Behavior, More Salient Games and O¤ense Types
To further improve our understanding of behavioral responses, we explore judges’ emotional reactions
to unexpected college football game outcomes along the dimensions of spatial behavior, salience of a
game and classi…cation of the o¤enses. Fan motivation and behavior may depend upon the types of fan.
Speci…cally, local fans exhibit fan like behavior because of their identi…cation with a geographic area
(Hunt et al. 1999). Since local fans are likely to be bound by geography, proximity may elicit emotional
arousal. Positive and negative reactions may further trigger when individuals are closer in distance to
the core of an action and when they feel they are integral part of it, the so-called spatial behavior. This
type of behavior in our current context suggests a positive relationship between emotional arousal and
geographical proximity. To explore this argument, we measure the distance in miles from the LSU football
stadium to parish clerk of court o¢ ce where dispositions were held. We then partition the data into two
based on the distance as (i) parish clerk of court o¢ ce within the 80 miles radius from the stadium, and
(ii) parish clerk of court o¢ ce farther than 80 miles. The results from this exercise are reported in the
…rst two columns of Table 6. The e¤ect of an upset loss on disposition length set by the judges is about
…ve times as large for closer parishes compared to those farther away (the di¤erence is not statistically
signi…cant though). The coe¢ cient estimates for the e¤ects of close losses and upset wins are smaller in
magnitude and are not di¤erent from zero in Columns 1 and 2 of Table 6.
Next, we classify games based on their salience. Speci…cally, we consider a game to be more salient if
LSU college football team is ranked in the top 10 prior to the actual game according to Associated Press
rankings. Columns 3 and 4 of Table 6 present the results by the nature of the game. It appears that
judges are harsher if the team su¤ers an unexpected loss from a more salient game. Among such games,
an upset loss indicates 55 days longer disposition length imposed by the judges, while the impact is only
7 days for less salient games and it is not statistically di¤erent from zero. We also examine emotional
cues by home game status and …nd no di¤erence between home and away games.
Finally, we explore judges’emotional responses to unexpected game outcomes by broad o¤ense types.
12
Using the OJJ’s broad classi…cation of crimes, we group o¤enses as felony and non-felony. The last two
columns of Table 6 provide the coe¢ cient estimates by severity of the o¤enses. Judges seem to be harsher
following an upset loss if the disposition length is set for a felony. Speci…cally, the estimated e¤ect of an
upset loss for a felony is more than twice of that observed for a non-felony crime. We continue to observe
no statistically signi…cant impact from a close loss or an upset win on disposition length set by the judge,
irrespective of the o¤ense type.
5.3
Heterogeneous E¤ects
In this section, we extend our analysis to investigate whether there are any di¤erential e¤ects of emotional cues on judicial decision among subgroups. We explore the heterogeneity along the dimensions
of judge/juvenile’s gender and race. Table 7 reports the results. We do not observe any substantial
heterogeneity with respect to the gender of either the judge (Columns 1 and 2) or juvenile (Columns 5
and 6). Turning to the estimated e¤ects by race, we …nd the impact of an upset loss to be more precisely
estimated for disposition length imposed by the white judges. Moreover, almost a …ve times larger effect from an upset loss over a close loss observed for the subsample of white judges provides additional
evidence for expectations around a reference point. Looking at the e¤ects by juvenile race (Columns 7
and 8), we …nd judges to be harsher on black juveniles following an upset loss on a Saturday game. The
estimated e¤ect indicates a statistically signi…cant 43 days longer disposition length and the coe¢ cient
estimate is more than twice of that for white juveniles. This racial disparity in judge sentencing following
an upset loss also suggests further unequal treatment of minority defendants and highlights the potential
arbitrariness in the application of the law. Courtroom outcomes may not be color blind.
We also examine heterogeneity by the law school judges graduated from. We do not …nd any large
discrepancy in the coe¢ cient estimates of cuing e¤ects for judges who graduated from LSU and other
law schools.
13
5.4
Additional Estimates and Threat of Selection Bias
In addition to their e¤ects on disposition length, emotional cues generated by unexpected wins and losses
on college football game outcomes may also have an impact on juveniles’propensity to be incarcerated
(disposition type). To shed light on this issue, we de…ne an indicator variable that takes the value of
one if the disposition set by the judge was to incarcerate (secure or non-secure custody) the youth.
In this analysis, the outcome in equation (1) is the indicator for incarceration. The results from this
exercise are provided in Table 8. None of the coe¢ cient estimates associated with the e¤ects of emotional
cues are statistically di¤erent from zero. Our further examination of the e¤ects of emotional shocks on
the probability of incarceration by geographical proximity, nature of the game, broad o¤ense type and
juvenile/judge characteristics do not alter our …ndings. All these additional results are available upon
request.12
Thus far, we have not addressed the potential contamination in the coe¢ cient estimates that may
arise due to sample selection bias. Recall that, a youth exits the OJJ system if a judge dismisses a
case. Unfortunately, our data do not allow us to observe case records when a judge dismisses a case.
This may lead to a selective sample. Under the scenario that emotional cues from game outcomes a¤ect
the likelihood of a judge dismissing the case (juvenile exiting the OJJ system) and assuming that the
e¤ects of emotional shocks from this potential selection move in the direction consistent with the rational
reference point behavior formation, one would expect the e¤ect of an upset or a close loss (upset win)
to be downward (upward) biased. This follows from the fact that the sample in the absence of any
(potential) selection stemming from an upset/close loss (win), on average, is likely to include case records
associated with less (more) severe o¤enses. It is a well-known fact that judges are more prone to exercise
their informal discretionary power for dismissal on borderline or grey area cases which usually involve
petty o¤enses (Robinson 2000; Bowers and Robinson 2012). Therefore, if any, the coe¢ cient estimates
on loss (win) measures reported throughout the paper are acting as lower (upper) bound estimates. Our
12
Our conclusions remain the same if we instead classify the incarceration dummy to only include secure-custody.
14
additional evidence against sample selection bias comes from the results obtained for severe o¤enses.
As noted, case dismissal is less of an issue when it comes to more severe o¤enses and that limiting our
attention to serious o¤enses is likely to imply a lower degree of contamination from selection bias (if
any). Going back and looking at the felony dispositions from Column 5 of Table 6, we only observe a
statistically signi…cant e¤ect for upset losses.
5.5
Emotional Cues and New Orleans Saints
Finally, we extend our analysis to see if the e¤ects of emotional cues on disposition length observed after
LSU football games carry over to other but similar large scale events. Speci…cally, we examine potential
emotional shocks driven by the outcomes of games played by the local NFL team-New Orleans Saints.
Table A2 in the Appendix reports New Orleans Saints win-loss records for all seasons from 1996 to 2012
and Table A3 provides predicted and actual game outcomes. Compared to LSU, Saints won a lower
fraction of games played at the professional league and upset losses were more common (34 percent).
The coe¢ cient estimate from a regression of actual spread on the predicted spread is 1.01 (0.14) with a
R2 value of 0.17. We estimate the emotional cuing e¤ects using equation (1) for New Orleans Saints by
limiting our attention to all dispositions imposed following a Sunday Saints game.13 The results from
this exercise are reported in Table 9 for a set of di¤erent speci…cations. Focusing on our most extensive
and preferred speci…cation from the last column of Table 9, we do not …nd any e¤ect of an upset loss or
an upset win on disposition length. Note also that the coe¢ cient estimate on the e¤ect of an upset loss
is very small in magnitude. The …ndings remain intact if we further explore the impact of emotional cues
on disposition length by geographical proximity, broad o¤ense type and juvenile/judge characteristics.
New Orleans Saints is only ranked 18 out of 32 NFL teams based on the local fan loyalty rankings
(USA Today, September 9, 2015). With its long history and traditions dating back to 1890s, LSU college
13
We also estimate the cuing e¤ects on disposition length by including full set of interactions between ex ante classi…cation
of games and outcomes of the games and treating the nongame weeks (bye week) as the omitted category. The results are
very similar to those reported in Table 9.
15
football plays a prominent role in the life of the local community. Feinswog (2013) states: “To call LSU
football a happening is an understatement. It is grained and woven into the sporting culture in Louisiana
in a way that boggles the mind.” The extent of fan devotion and emotional attachment may explain
the divergence in these two sets of results. Unexpected wins or losses by Saints may not translate into
emotional arousal due to lower degree of attachment and the lack of formation of self-concept with the
team.
5.6
Robustness Checks
We undertake several sensitivity checks to examine the validity of our results. First, as an alternative
to our discrete parametrization in equation (1), we include a cubic polynomial in the point spread and
an interaction between the polynomial and indicator for LSU football team loss (Card and Dahl 2011).
Figure 6 plots the estimated interaction e¤ect over a range of spread along with the associated pointwise
95% con…dence interval. The e¤ect of a loss on disposition length set by the judges is decreasing in the
spread and it is only statistically signi…cant for spread values roughly less than -2. Second, keeping our
discrete parametrization, we also experiment di¤erent cuto¤ values (e.g., -5 and 5) to describe unexpected
college football game outcomes. The …ndings of the paper from this exercise remain intact.
Third, we use a Poisson regression to obtain the cuing e¤ects on disposition length. Our results from
this alternative model speci…cation are consistent with those reported throughout the paper. Speci…cally,
an upset loss leads to a 5.8 percent increase (6.4 percent from our preferred speci…cation in the last
column of Table 4) in the disposition length and we do not observe any statistically signi…cant impact
from a close loss or an upset win. Fourth, performing a log transformation of the outcome variable or
trimming the distribution of sentence length for potential outliers (e.g., dropping the observations for the
bottom and top …ve quantiles of the sentence length distribution) do not a¤ect our …ndings. Recall also
that judges can set a maximum duration of disposition up to the youth’s 21st birthday. To alleviate any
concerns from potential truncation, we limit our attention to defendants age 16 or younger and doing so
16
has no e¤ect on our results either.14 Fifth, it is conceivable that the wave of sentiment prevailing during
the Hurricanes Katrina and Rita a¤ected the judicial decisions and game outcomes simultaneously. To
check this, we dropped all games played in 2005 and 2006 seasons. Doing so has almost no impact on
our estimated e¤ects. Similarly, dropping defendants residing out of state (2 percent of the sample) or
excluding bowl games from the model estimations produce virtually identical results.
Sixth, we did not include nongame weeks in our sample for estimates reported throughout the paper.
Treating these weeks (bye weeks) as the omitted category in equation (1) and adding full set of interactions
between ex ante classi…cation of games and outcomes of games yield identical results. Seventh, excluding
heinous crimes (i.e., …rst and second degree murder and aggravated rape) do not alter our conclusions.
Finally, to examine the extent to which our results are driven by the decisions of a single judge, we
repeatedly estimate equation (1) each time removing dispositions set by a di¤erent judge. Out of a
total of 207 regressions, the e¤ect of an upset loss on disposition length is always statistically signi…cant
whereas the coe¢ cient estimates for the e¤ects of a close loss or an upset win are never di¤erent from
zero. Moreover, in terms of magnitude, the e¤ect from an upset loss is generally at least twice as large
(in absolute value) as those obtained for two other measures. All these results are available upon request.
6
Conclusion
We analyze the behavior of a highly-educated group of professionals, the behavior of whom should, by
law, be free of person-speci…c reference points. More precisely, we examine the e¤ects of emotional
shocks associated with the unexpected outcomes of games played by Louisiana State University (LSU)
football team on judicial decisions handed down by juvenile court judges in the state of Louisiana. We
use information contained in the pregame point spread to determine fans’(judges) rational expectations
about the outcome of the game and to investigate departures from reference points on judicial decisions.
14
Further limitation of the sample to defendants age 15 or younger produce similar results, however, the e¤ects are less
precisely estimated.
17
Our ability to employ micro-level data further allows us to make inference on potential treatment disparity
based on observable juvenile and judge characteristics.
Using the universe of defendant …les obtained from the Louisiana O¢ ce of Juvenile Justice along with
the LSU football team records over a period of more than …fteen years, we reach to following conclusions.
Upset losses increase disposition length imposed by the judges by 6.4 percent and the e¤ect of an upset loss
on judicial decisions seems to persist throughout the week following a Saturday game. Close losses and
upset wins, on the other hand, have no signi…cant impact. These results taken together provide credible
evidence for expectation-based reference point formation/loss aversion. We also …nd the emotional cuing
e¤ects stemming from upset losses to be more pronounced among judges in parishes closer to home of
LSU, at more salient games, as well as for juvenile defendants with severe o¤enses. Finally, it appears
that the impact of emotional shocks on disposition length set by the judges are larger for black juveniles.
This racial disparity suggests further unequal treatment of minority defendants and presumably raises
concerns in the equal protection clause of the law. Several robustness checks support our …ndings.
Researchers are increasingly trying …nd ways to test the relevance of models of reference-based preferences in settings outside the laboratory environment. Our results using a natural experiment in the
absence of any e¤ort provision add to this growing body of research. From a broader perspective, we
…nd a non-negligible support for capricious application of punishment by judges, at least in the state of
Louisiana. Unequal treatment by the courts can undermine the legitimacy of the justice system and it can
damage the trust in state institutions, which can in turn lead to an equilibrium of increased recidivism,
as well as antisocial, dishonest and illicit behavior. Unequal treatment in the judicial system has also
implications for a wide-range of economic and social outcomes including human capital formation, labor
market activity and family formation.
It may be premature to propose any policy recommendation on judicial decisions handed down by
judges without fully considering private costs and any potential spillover e¤ects to the society in general.
That being said, a panel involving judges of di¤erent views and experiences and diversi…ed backgrounds
18
may reduce the arbitrariness in the application of the law.
19
References
Abeler, J., A. Falk, and D. Hu¤man (2011). Reference Points and E¤ort Provision. American Economic
Review 101 (2), 470–492.
Abrams, D., M. Bertrand, and S. Mullainathan (2012). Do Judges Vary in Their Treatment of Race?
Journal of Legal Studies 41 (2), 347–383.
Aizer, A. and J. J. Doyle (2013). Juvenile Incarceration, Human Capital and Future Crime: Evidence
from Randomly-Assigned Judges. Quartely Journal of Economics forthcoming.
Banerji, A. and N. Gupta (2014). Detection, Identi…cation, and Estimation of Loss Aversion: Evidence
from an Auction Experiment. American Economic Journal: Microeconomics 6 (1), 91–133.
Bowers, J. and P. H. Robinson (2012). Perceptions of Fairness and Justice: The Shared Aims and
Occasional Con‡icts of Legitimacy and Moral Credibility. Wake Forest Law Review 47 (2), 211–284.
Card, D. and G. B. Dahl (2011). Family Violence and Football: The E¤ect of Unexpected Emotional
Cues on Violent Behavior. Quartely Journal of Economics 126 (1), 103–143.
Chase, C. (2015). The Most Loyal Fans in the NFL: Ranked. USA Today September 9, 2015.
Crawford, V. P. and J. Meng (2011). New York City Cab Drivers’Labor Supply Revisited: ReferenceDependent Preferences with Rational-Expectations Targets for Hours and Income. American Economic
Review 101 (5), 1912–1932.
Feinswog, L. (2013). Tales from the LSU Tiger Sideline: A Collection of Greatest Tiger Stories Ever
Told. Sports Publishing.
Gill, D. and V. Prowse (2012). A Structural Analysis of Disappointment Aversion in a Real E¤ort
Competition. American Economic Review 102 (1), 469–503.
20
Hockenberry, S. and C. Puzzanchera (2014). Juvenile Court Statistics. National Center for Juvenile
Justice.
Hossain, T. and J. List (2012). The Behavioralist Visits the Factory: Increasing Productivity Using
Simple Framing Manipulations. Management Science 58 (12), 2151–2167.
Hunt, K. A., T. Bristol, and E. R. Bashaw (1999). A Conceptual Approach to Classifying Sports Fans.
Journal of Services Marketing 13 (6), 439–452.
Kahneman, D. and A. Tversky (1979). Prospect Theory: An Analysis of Decision under Risk. Econometrica 47 (2), 263–292.
Koszegi, B. and M. Rabin (2006). A Model of Reference-Dependent Preferences. Quartely Journal of
Economics 121 (4), 1133–1165.
Levitt, S. D. and J. List (2009). Field Experiments in Economics: The Past, The Present, and The
Future. European Economic Review 53 (1), 1–18.
Louisiana Children’s Code (1996-2012). Louisiana State Legislature-Various Chapters.
Louisiana District Judges Association Periodicals (1956-2000). Guide to the Louisiana Judiciary.
Miller, T. Q., L. Heath, J. R. Molcan, and B. L. Dugoni (1991). Imitative Violence in the Real World:
A Reanalysis of Homicide Rates Following Championship Prize Fights. Aggressive Behavior 17 (3),
121–134.
Phillips, D. P. (1983). The Impact of Mass Media Violence on U.S. Homicides. American Sociological
Review 48 (4), 560–568.
Pope, D. G. and M. E. Schweitzer (2011). Is Tiger Woods Loss Averse? Persistent Bias in the Face of
Experience, Competition, and High Stakes. American Economic Review 101 (1), 129–157.
21
Robinson, P. H. (2000). Why Does the Criminal Law Care What the Lay Person Thinks Is Just? Coercive
versus Normative Crime Control. Virginia Law Review 86 (8), 1838–1869.
Scott, L. C. (2014). It’s Not Just Entertainment: The Economic Impact of the LSU Athletic Department
on the Baton Rouge MSA-Policy Report.
22
23
207
0.320
0.320
0.420
9.542
0.444
0.500
379.78
0.454
0.480
0.474
0.416
1.478
0.495
SD
The full sample statistics are available upon request. See text for further details.
judges. The variables are only a subset of those used in the analysis. The remainder are excluded in the interest of brevity.
the week following a Saturday LSU football game for seasons from 1996 to 2012 and their corresponding disposition
NOTES: The statistics above reflect our research sample, which consists of juveniles who were disposed before the judge
Number of Judges
Black
White
Female
Age
Party Affiliation-Democratic Party
LSU Law School
0.115
0.885
0.227
56.196
0.726
0.473
10,671
Sample Size
Panel B: Judge Characteristics
529.87
0.292
0.638
0.342
0.223
14.837
0.430
Mean
Disposition Length
Incarceration (Secure and Non-secure Custody)
Black
White
Female
Age
Committed a Felony
Panel A: Juvenile Characteristics
Table 1: Summary Statistics for Juveniles and Judges
24
11-2
2006
2005
11-2
9-3
10-2
1997
12-2
2007
4-7
1998
8-5
2008
3-8
1999
9-4
2009
8-4
2000
NOTES: Win-Loss records include all season games and bowl and championship games from 1996 to 2012.
LSU Season Record (Win-Loss)
LSU Season Record (Win-Loss)
1996
SEASONS
Table 2: LSU Football Games Win-Loss Record for Seasons from 1996 to 2012
11-2
2010
10-3
2001
13-1
2011
8-5
2002
10-3
2012
13-1
2003
9-3
2004
25
78
43.6
68.1
[65.3]
11.5
[14.8]
16.2
[15.9]
48.3
[45.1]
15.6
[18.8]
35.7
[35.6]
3.9
7
122
[6,970]
14
[1,032]
29
[1,697]
14
[767]
28
[2,004]
10
[714]
25.7
74.3
85.1
14.8
46
133
184
32
% of Category
NOTES: Win-Loss records include all season games and bowl and championship games from 1996 to 2012. Associated
Press rankings are for the top 25 college football teams and the rankings are published every Sunday during the college
football season. See text for further details.
Associated Press Rankings
Football Games LSU Ranked Top 10
Actual Win (Upset Win)
Predicted Loss: point spread 4 or more
Actual Loss (Close Loss)
Predicted Close:-4<point spread<4
Actual Loss (Upset Loss)
Predicted Outcomes
Predicted Win: point spread -4 or less
Bowl Games
Outcome
Loss
Win
Panel B: Saturday Games
Football Games on Saturday
Football Games on Other Days
Panel A: LSU Football Games
Number of Games
[# of Dispositions]
Table 3: Summary Statistics for LSU Football Games for Seasons from 1996 to 2012
26
Yes
No
No
No
No
No
10,671
36.582**
(17.562)
36.893
(22.867)
-25.535
(26.083)
-12.579
(15.484)
13.520
(17.925)
(1)
Yes
Yes
No
No
No
No
38.129**
(17.466)
33.419
(22.929)
-26.381
(25.910)
-9.965
(15.482)
16.169
(17.926)
(2)
Yes
Yes
Yes
No
No
No
38.918**
(17.184)
35.597
(22.218)
-20.047
(25.786)
-17.083
(14.979)
14.887
(17.848)
Yes
Yes
Yes
Yes
No
No
37.564**
(17.177)
33.991
(22.245)
-23.519
(26.439)
-11.941
(16.053)
21.294
(19.215)
Coefficients
(Standard Errors)
(3)
(4)
Yes
Yes
Yes
Yes
Yes
No
33.576**
(15.777)
16.017
(18.739)
-16.330
(23.587)
2.618
(14.294)
24.303
(17.290)
(5)
Yes
No
Yes
Yes
Yes
Yes
34.405**
(16.164)
19.150
(16.837)
-7.121
(23.534)
-14.865
(13.182)
1.681
(15.501)
(6)
week day×week×season level. Judge controls include indicators for judge's gender, race/ethnicity and political party affiliation and judge's age and its square. Juvenile controls include
indicators for juvenile's gender and race/ethnicity and juvenile's age and its square. The game controls include indicators for home and bowl games. There are 178 detailed offense types
and 207 judges in the effective sample. Judge fixed effect specification include time varying characteristics (indicator for party affiliation, age and its square). Predicted win is the omitted
category.
*significant at 10%, ** significant at 5%, *** significant at 1%.
NOTES: The sample is restricted to all juvenile dispositions following a Saturday football game for seasons from 1996 to 2012. Predicted win indicates a point spread of -4 or less, predicted
close indicates a point spread between -4 and 4 (exclusive) and predicted loss indicates a point spread of 4 or more. Standard errors are reported in parentheses and are clustered at the
Controls:
Season, Week, and Days of Week
Judge
Juvenile
Game
Offense Fixed Effects
Judge Fixed Effects
Sample Size
Predicted Loss
Predicted Close
Win×Predicted Loss (Upset Win)
Loss×Predicted Close (Close Loss)
Loss×Predicted Win (Upset Loss)
Dependent Variable: Disposition Length
Table 4: The Effects of Emotional Shocks from LSU Football Games on Disposition Length Imposed by the Judges
27
Yes
No
Yes
Yes
Yes
Yes
33.175**
(16.519)
9.340
(45.305)
13.179
(17.607)
51.005
(40.533)
-13.328
(24.453)
58.339
(54.770)
-14.814
(13.178)
1.664
(15.487)
(1)
Coefficients
(Standard Errors)
Yes
No
Yes
Yes
Yes
Yes
24.330
(18.759)
16.503
(25.153)
23.804
(25.659)
-7.882
(28.961)
-22.014
(31.579)
29.354
(38.655)
-15.217
(13.172)
1.369
(15.444)
(2)
Early Week={Mon., Tu.,
and Wed.}
NOTES: Standard errors are reported in parentheses and are clustered at the week day×week×season level. Early week indicator from
column (1) only includes Monday, while it includes Monday through Wednesday for column (2). See notes to Table 4 and the text
for data and control variable details.
*significant at 10%, ** significant at 5%, *** significant at 1%.
Controls:
Season, Week, and Days of Week
Judge
Juvenile
Game
Offense Fixed Effects
Judge Fixed Effects
Predicted Loss
Predicted Close
Win×Predicted Loss×Early Week Days
Win×Predicted Loss (Upset Win)
Loss×Predicted Close×Early Week Days
Loss×Predicted Close (Close Loss)
Loss×Predicted Win×Early Week Days
Loss×Predicted Win (Upset Loss)
Dependent Variable: Disposition Length
Early Week={Mon.}
Table 5: The Effects of Emotional Shocks from LSU Football Games on Disposition Length Imposed by the
Judges by Week Days
28
Yes
No
Yes
Yes
Yes
Yes
Controls:
Season, Week, and Days of Week
Judge
Juvenile
Game
Offense Fixed Effects
Judge Fixed Effects
Yes
No
Yes
Yes
Yes
Yes
5,290
9.955
(24.257)
8.990
(24.163)
-18.090
(32.673)
-11.083
(20.670)
-4.277
(23.769)
(2)
Yes
No
Yes
Yes
Yes
Yes
3,954
55.384*
(29.371)
48.083
(33.456)
40.283
(59.024)
-25.612
(21.315)
-23.063
(29.537)
Coefficients
(Standard Errors)
(3)
Yes
No
Yes
Yes
Yes
Yes
6,717
6.529
(25.276)
10.657
(24.271)
-10.607
(27.540)
-19.544
(18.853)
-2.047
(21.617)
(4)
Game Type
LSU
LSU
Ranking<10
Ranking>=10
Yes
No
Yes
Yes
Yes
Yes
4,594
51.075**
(25.458)
46.614
(31.685)
-36.146
(36.271)
-28.781
(26.515)
-4.230
(27.085)
(5)
Yes
No
Yes
Yes
Yes
Yes
6,077
19.336
(17.960)
18.8665
(22.016)
15.025
(27.544)
-15.359
(15.322)
4.105
(17.878)
(6)
Offense Type
Felony
Non-Felony
NOTES: Standard errors are reported in parentheses and are clustered at the week day×week×season level. Distances are measured from LSU football stadium to the parish clerk of court
office. A game is considered as salient if LSU football team is ranked at the top ten in the Associated Press weekly rankings. Offense classifications (felony and non-felony) are based on
the Louisiana Office of Juvenile Justice categorization. See notes to Table 4 and the text for data and control variable details.
*significant at 10%, ** significant at 5%, *** significant at 1%.
5,381
47.870**
(22.272)
24.830
(27.378)
-2.054
(32.048)
-20.567
(16.962)
5.177
(21.501)
(1)
Sample Size
Predicted Loss
Predicted Close
Win×Predicted Loss (Upset Win)
Loss×Predicted Close (Close Loss)
Loss×Predicted Win (Upset Loss)
Dependent Variable: Disposition Length
Distance to LSU Stadium
Distance=<80 Miles
Distance>80 Miles
Table 6: The Effects of Emotional Shocks from LSU Football Games on Disposition Length Imposed by the Judges by Distance, Salience of a Game
and Offense Type
29
Yes
No
Yes
Yes
Yes
Yes
Controls:
Season, Week, and Days of Week
Judge
Juvenile
Game
Offense Fixed Effects
Judge Fixed Effects
Yes
No
Yes
Yes
Yes
Yes
3,155
27.828
(29.647)
37.552
(31.355)
45.816
(35.773)
-30.480
(25.904)
-17.039
(29.177)
(2)
Yes
No
Yes
Yes
Yes
Yes
1,919
65.994
(42.384)
78.405
(64.658)
24.564
(65.171)
-6.976
(38.907)
-7.460
(43.214)
(3)
Judge Characteristics
Female
Black
Male
Yes
No
Yes
Yes
Yes
Yes
8,752
30.302*
(16.657)
6.191
(17.687)
-21.309
(22.302)
-16.579
(14.502)
8.763
(16.972)
Yes
No
Yes
Yes
Yes
Yes
8,282
36.618*
(19.483)
30.906
(19.587)
-8.889
(27.845)
-15.394
(15.905)
-4.827
(19.088)
Coefficients
(Standard Errors)
(4)
(5)
White
Yes
No
Yes
Yes
Yes
Yes
2,389
32.070
(27.941)
-9.154
(38.935)
6.984
(43.595)
-16.561
(30.288)
16.239
(29.229)
(6)
Yes
No
Yes
Yes
Yes
Yes
6,811
42.945**
(20.144)
15.150
(22.997)
5.853
(28.894)
-18.858
(17.578)
3.209
(20.086)
(7)
Juvenile Characteristics
Female
Black
Yes
No
Yes
Yes
Yes
Yes
3,652
19.364
(22.687)
24.835
(27.240)
-10.890
(32.353)
-11.077
(21.944)
1.419
(24.161)
(8)
White
*significant at 10%, ** significant at 5%, *** significant at 1%.
NOTES: Standard errors are reported in parentheses and are clustered at the week day×week×season level. See notes to Table 4 and the text for data and control variable details.
7,516
37.034*
(20.970)
4.428
(20.500)
-29.741
(32.464)
-8.879
(16.083)
9.815
(20.007)
(1)
Sample Size
Predicted Loss
Predicted Close
Win×Predicted Loss (Upset Win)
Loss×Predicted Close (Close Loss)
Loss×Predicted Win (Upset Loss)
Dependent Variable: Disposition Length
Male
Table 7: The Effects of Emotional Shocks from LSU Football Games on Disposition Length Imposed by the Judges by Judge
and Juvenile Characteristics
30
Yes
No
No
No
No
No
10,671
0.017
(0.025)
0.015
(0.030)
-0.012
(0.033)
0.004
(0.022)
0.025
(0.025)
(1)
Yes
Yes
No
No
No
No
0.018
(0.025)
0.021
(0.030)
-0.017
(0.032)
-0.000
(0.022)
0.025
(0.024)
(2)
Yes
Yes
Yes
No
No
No
0.020
(0.024)
0.012
(0.030)
-0.018
(0.032)
0.003
(0.021)
0.029
(0.024)
Yes
Yes
Yes
Yes
No
No
0.019
(0.024)
0.012
(0.029)
-0.018
(0.032)
0.000
(0.022)
0.026
(0.024)
Coefficients
(Standard Errors)
(3)
(4)
Yes
Yes
Yes
Yes
Yes
No
0.023
(0.024)
0.006
(0.028)
-0.021
(0.031)
0.007
(0.021)
0.029
(0.023)
(5)
Yes
No
Yes
Yes
Yes
Yes
0.013
(0.019)
-0.004
(0.024)
-0.025
(0.025)
0.014
(0.017)
0.046**
(0.020)
(6)
NOTES: Standard errors are reported in parentheses and are clustered at the week day×week×season level. See notes to Table 4 and the text for data and control variable details.
*significant at 10%, ** significant at 5%, *** significant at 1%.
Controls:
Season, Week, and Days of Week
Judge
Juvenile
Game
Offense Fixed Effects
Judge Fixed Effects
Sample Size
Predicted Loss
Predicted Close
Win×Predicted Loss (Upset Win)
Loss×Predicted Close (Close Loss)
Loss×Predicted Win (Upset Loss)
Dependent Variable: Incarceration
(Secure and Non-Secure Custody)
Table 8: The Effects of Emotional Shocks from LSU Football Games on Disposition Type (Incarceration) Imposed by the Judges
31
Yes
No
No
No
No
No
13,718
13.191
(20.358)
-13.182
(12.821)
-16.674
(16.005)
22.456
(16.173)
25.211
(18.164)
(1)
Yes
Yes
No
No
No
No
13.641
(20.270)
-13.257
(12.835)
-12.543
(15.978)
22.171
(16.261)
23.546
(18.160)
(2)
Yes
Yes
Yes
No
No
No
11.570
(20.443)
-9.090
(12.637)
-10.510
(15.571)
22.872
(16.062)
25.516
(17.729)
Yes
Yes
Yes
Yes
No
No
9.827
(20.430)
-8.954
(12.604)
-9.927
(15.590)
25.336
(16.203)
33.649
(18.676)
Coefficients
(Standard Errors)
(3)
(4)
Yes
Yes
Yes
Yes
Yes
No
20.060
(16.736)
-4.903
(11.354)
-15.094
(13.766)
25.155*
(13.706)
34.697**
(16.013)
(5)
Yes
No
Yes
Yes
Yes
Yes
3.603
(15.241)
-18.540*
(10.188)
-10.068
(13.223)
19.960*
(11.542)
19.791
(13.807)
(6)
NOTES: The sample is restricted to all juvenile dispositions following a Sunday football game for seasons from 1996 to 2012. Standard errors are reported in parentheses and are clustered
at the week day×week×season level. There are 187 detailed offense types and 209 judges in the effective sample. See notes to Table 4 and the text for data and control variable details.
*significant at 10%, ** significant at 5%, *** significant at 1%.
Controls:
Season, Week, and Days of Week
Judge
Juvenile
Game
Offense Fixed Effects
Judge Fixed Effects
Sample Size
Predicted Loss
Predicted Close
Win×Predicted Loss (Upset Win)
Loss×Predicted Close (Close Loss)
Loss×Predicted Win (Upset Loss)
Dependent Variable: Disposition Length
Table 9: The Effects of Emotional Shocks from New Orleans Saints Football Games on Disposition Length Imposed by the Judges
4000
3000
Frequency
2000
1000
0
0
250
500
750
1000
Disposition Length
1250
1500
Figure 1: Distribution of Disposition Length Imposed by the Judges
NOTES: All dispositions are during the weekdays following a Saturday game for seasons from 1996 to 2012.
32
40
Realized Score Differential
-20
0
20
-40
-60
predicted
close
predicted win
-40
-20
Point Spread
-4
0
predicted loss
4
20
Figure 2: Realized Score Di¤erential (Opponent Team-LSU) and Pregame Point Spread
NOTES: The plotted regression has a slope of 0.98 (s.e.=0.07). The
33
R2 from the regression is 0.49.
2
Frequency
4
6
8
Opponent Team
Panel B: Actual Loss (Upset Loss)
WESTERN KENTUCKY
WEST VIRGINIA
WASHINGTON
VIRGINIA TECH.
VANDERBILT
NOTRE DAME
MISSISSIPPI
NOTES: Predicted win denotes games where point spread for LSU is -4 or less.
UTAH ST.
Figure 3: Predicted Win and Upset Loss
UL-MONROE
KENTUCKY
IOWA
Opponent Team
UL-Lafayette
TULANE
TROY
TROY
TOWSON
TENNESSEE
SOUTH CAROLINA
SAN JOSE ST
OREGON ST.
GEORGIA
FLORIDA
AUBURN
ALABAMA at BIRMINGHAM
ALABAMA
34
NOTRE DAME
NORTH TEXAS
NORTH CAROLINA
NEW MEXICO ST.
MISSISSIPPI ST.
MISSISSIPPI
MIDDLE TENNESSEE
MIAMI-OHIO
MCNEESE ST
LA TECH.
KENTUCKY
IOWA
IDAHO
HOUSTON
GEORGIA
FRESNO ST
FLORIDA
EL PASO
CLEMSON
AUBURN
ARKANSAS ST.
ARIZONA
ALABAMA at BIRMINGHAM
ALABAMA
AKRON
0
0
2
4
Frequency
6
8
10
12
14
Panel A: Predicted Win
0
2
4
Frequency
6
8
10
12
14
Panel A: Predicted Close
TEXAS A&M
TENNESSEE
SOUTH CAROLINA
OREGON
NOTRE DAME
MISSISSIPPI ST.
MISSISSIPPI
KENTUCKY
HOUSTON
GEORGIA
FLORIDA
AUBURN
ARKANSAS
ARIZONA ST.
ALABAMA
Opponent Team
0
2
Frequency
4
6
8
Panel B: Actual Loss (Close Loss)
NOTRE DAME
MISSISSIPPI
KENTUCKY
HOUSTON
GEORGIA
FLORIDA
AUBURN
ARKANSAS
ALABAMA
Opponent Team
Figure 4: Predicted Close and Close Loss
NOTES: Predicted close denotes games where point spread for LSU is between -4 and 4 (exclusive).
35
0
2
4
Frequency
6
8
10
12
14
Panel A: Predicted Loss
TENNESSEE
MISSISSIPPI ST.
MISSISSIPPI
GEORGIA
FLORIDA
AUBURN
ALABAMA
Opponent Team
0
2
Frequency
4
6
8
Panel B: Actual Win (Upset Win)
Figure 5: Predicted Loss and Upset Win
NOTES: Predicted loss denotes games where point spread for LSU is 4 or more.
36
TENNESSEE
MISSISSIPPI ST.
FLORIDA
AUBURN
ALABAMA
Opponent Team
75
Increase in Disposition Length
-25
0
25
50
-50
predicted
close
predicted win
-12
-8
-4
0
Point Spread
predicted loss
4
8
12
Figure 6: Increase in Disposition Length for a LSU Loss vs. a Win as a Function of the Pregame Point
Spread
NOTES: The estimates are obtained from a speci…cation with a cubic-order polynomial in the point spread and an interaction
between the polynomial and an indicator for LSU loss. The dashed lines are pointwise 95% con…dence intervals.
37
…..
…..
…..
…..
7,643
Loss×Predicted Close (Close Loss)-Week Before
Win×Predicted Loss (Upset Win)-Week Before
Predicted Close-Week Before
Predicted Loss-Week Before
Sample Size
38
14.548
(24.134)
4.204
(24.369)
-4.675
(23.715)
4.085
(18.578)
-8.346
(17.890)
…..
…..
…..
…..
…..
Coefficients
(Standard Errors)
(2)
35.303**
(17.621)
19.880
(20.945)
-14.369
(28.771)
-43.182**
(17.574)
3.086
(18.714)
14.851
(24.533)
3.684
(24.275)
-8.090
(23.504)
3.270
(18.625)
-16.007
(18.043)
(3)
*significant at 10%, ** significant at 5%, *** significant at 1%.
and control variable details.
fixed effects. There are 165 detailed offense types and 205 judges in the effective sample. See notes to Table 4 and the text for data
controls for day of the week, week and season effects, (time-invariant) judge, juvenile and game characteristics and offense and judge
1996 to 2012. Standard errors are reported in parentheses and are clustered at the week day×week×season level. All specifications
NOTES: The sample is restricted to all juvenile dispositions following Saturday games played in consecutive weeks for seasons from
Predicted Loss
Predicted Close
Win×Predicted Loss (Upset Win)
Loss×Predicted Close (Close Loss)
Loss×Predicted Win (Upset Loss)-Week Before
(1)
34.589*
(18.855)
15.928
(20.468)
-3.834
(27.617)
-37.109**
(16.852)
4.608
(18.508)
…..
Loss×Predicted Win (Upset Loss)
Dependent Variable: Disposition Length
Table A1: The Long-Run Effects of Emotional Shocks from LSU Football Games on Disposition Length
Imposed by the Judges
Appendix: Addtional Results
39
11-7
2006
2005
3-13
6-10
3-13
1997
7-9
2007
6-10
1998
NOTES: Win-Loss records include all season, playoff and championship games from 1996 to 2012.
New Orleans Saints Season Record (Win-Loss)
New Orleans Saints Season Record (Win-Loss)
1996
8-8
2008
3-13
1999
16-3
2009
11-7
2000
SEASONS
Table A2: New Orleans Saints Football Games Win-Loss Record for Seasons from 1996 to 2012
11-6
2010
7-9
2001
14-4
2011
9-7
2002
7-9
2012
8-8
2003
8-8
2004
40
68
[3,364]
23
[1,085]
105
[5,909]
57
[3,012]
69
[4,445]
22
[1,665]
127
115
249
32
28.0
[24.5]
33.8
[32.3]
43.5
[43.0]
54.2
[51.0]
28.5
[32.5]
31.9
[37.4]
52.5
47.5
88.6
11.4
% of Category
NOTES: Win-Loss records include all season, playoff and championship games from 1996 to 2012. See text for further
details.
Actual Win (Upset Win)
Predicted Loss: point spread 4 or more
Actual Loss (Close Loss)
Predicted Close:-4<point spread<4
Actual Loss (Upset Loss)
Predicted Outcomes
Predicted Win: point spread -4 or less
Outcome
Loss
Win
Panel B: Sunday Games
Football Games on Sunday
Football Games on Other Days
Panel A: New Orleans Saints Football Games
Number of Games
[# of Dispositions]
Table A3: Summary Statistics for New Orleans Saints Football Games for Seasons from 1996 to 2012