Dahl and Card, 2009, “Family Violence and
Football: The Effect of Unexpected Emotional
Cues on Violent Behavior” NBER WP.
Presented by Joseph Guse, Econ 398
Fall 2010
Model
• q Pr( “conflictual interaction” )
• h Pr ( “losing control” )
– qh = probability of violent behavior
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y = 1 (“home team wins”)
p = Pr ( “home team win “)
h = hL = h0 – a(y-p) if LOSE
h = =hW =h0 – b(y-p) if WIN
Assume a > b. Loss Aversion. Disappointment is a
stronger emotional cue than relief.
Empirical Strategy
• “Police Reported episodes of family violence
in a set of cities .. With a ‘home’ NFL team”
• Poisson Model
• 3 Categories for Predicted Outcome
• Predicted Loss (by 3 or more points)
• Prediced Win (by 3 or more points)
• Predicted Close
Interacted with dummies for win or loss.
Data
•
NIBRS. National Incident-Based Reporting System (Table 1 for descriptive
stats)
– Victim Info (age, gender, injured)
– Offender (gender, relationship to victim)
– TOD, Location1
•
Link Reporting Police Agency to a home NFL Team
– Many big cities not in NIBRS, so focus on states with a single NFL team.
Less powerful due to further distance from stadium? Are Beloit residents
less into Packers than Green Bay residents?
– Six Teams in Sample. (Tables 2 & 3 for descriptive stats)
• 993 Reg Season Game, 53 Playoff Games – most on Sunday
•
LV Point Spreads & “Salience” (40%: rival, playoff contention, turnovers)
•
– See Figures 2 & 3 for descriptive stats.
Nielsen Ratings. 25% of all HHs tune in. Correlated with spread (Fig 4)
Regression Equation
Upset Loss
Close Loss
Upset Win
Results
• Table 4 Baseline Regression Results
• Table 5. Distinguish between time of game (1
or 4 pm) and Time of Violence.
– 1 pm games -> violence in 3-6pm (upset loss)
– 4pm game -> violence in 6-9pm (upset loss)
– 4pm games -> LESS violence in 6-9 (upsetWIN)
• Table 6. Salience
– Close loss in salient games increase violence
– Note: upset WINS against rival increase violence??
Things I like about this paper.
• Contributes significantly to our understanding of an important
issue (domestic violence).
– See Their discussion section for an excellent example of how to draw
conclusions from results and fit them into the broader literature.
• A great melding of various data sources. (NBIR, gambling
market, weather, NFL)
• Every robustness check you would ask for and more.
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Alternate spec of (winprob) interacted with (win).
Time of day analysis.
Alternate hazard model (negative binomial)
Alternate treatment of dep var = 0.
Room for improvement
• Nielsen Rating variable. Theory predicts that this
should roughly scale up effects of game
characteristics, but they enter it as a separate term in
the hazard function. TV audience size is one of their
X’s:
Log(mjt) = qj + Xjtg + g(Sjt, yjt; l)
Should it be more like this?
Log(mjt) = qj + Zjtg + v*g(Sjt, yjt; l)
Where v is TV audience size and X = {Z,v}. Or maybe
not since v is already correlated with spread?