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Presented by Joseph Guse, Econ 398 Fall 2010

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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Presented by Joseph Guse, Econ 398 Fall 2010

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  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 • q Pr( “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.

  3. 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.

  4. 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)

  5. Regression Equation Upset Loss Close Loss Upset Win

  6. 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??

  7. 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.

  8. 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?

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