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Dahl and Card, 2009, Family Violence and Football: The Effect of Unexpected Emotional Cues on Violent Behavior NBER WP. Presented by Joseph Guse, Econ.

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Presentation on theme: "Dahl and Card, 2009, Family Violence and Football: The Effect of Unexpected Emotional Cues on Violent Behavior NBER WP. Presented by Joseph Guse, Econ."— Presentation transcript:

1 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

2 Model qPr( conflictual interaction ) h Pr ( losing control ) – qh = probability of violent behavior 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.

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4 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.

5 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)

6 Regression Equation Upset Loss Close Loss Upset Win

7 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??

8 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. – Alternate spec of (winprob) interacted with (win). – Time of day analysis. – Alternate hazard model (negative binomial) – Alternate treatment of dep var = 0.

9 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 Xs: Log( jt ) = j + X jt g(S jt, y jt ; Should it be more like this? Log( jt ) = j + Z jt v*g(S jt, y jt ; Where v is TV audience size and X = {Z,v}. Or maybe not since v is already correlated with spread?


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