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The Price of Violence

The Price of Violence. Long term effects of assault on labor force participation and health. Petra Ornstein, Uppsala university. Violence is bad, but how bad?. Information on the impact of assault is useful for: Meeting the needs of assault victims

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The Price of Violence

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  1. The Price of Violence Long term effects of assault on labor force participation and health Petra Ornstein, Uppsala university

  2. Violence is bad, but how bad? Information on the impact of assault is useful for: • Meeting the needs of assault victims • Policy decisions through information on expected benefit of crime reduction strategies • Understanding of violence as traumatic event The Price of Violence

  3. Identification problem Robust connection between assault and ill-health (in women), but what are the causal effects? • Focus: Violence -> ill-health Lack of studies on causal link! (Ehrensaft et al, 2006; Kilpatrick et al., 1997; Lindhorst and Oxford, 2008; Stevenson & Wolfers, 2006) • Reverse causation substance abuse -> violence exposure (Kilpatrick et al, 1997) • Confounding Lack of resources -> stress -> ill health (Aizer, 2010) -> violence exposure The Price of Violence

  4. Identification strategy • Large-scale and high-quality longitudinal data • Follow individuals previous to assault • Rich background information • Propensity score matching Improvements on previous studies • Identification • Including men • Full population of assaulted • Long term effects • Heterogenous effects (sex & employment) • Objective measures The Price of Violence

  5. Setup DATA • Micro data from administrative registers • LOUISE/LISA: Swedish population 16-64 years 1994-2008 • Cause of death Register • National Inpatient Care Register SELECTED SAMPLE • Registered in Sweden at least two consecutive years in 1997-2002 • Not hospitalized because of assault previous to 1998 • Cases have been hospitalized with cause of injury ”assault” in 1998-2002 • Matches are selected on information 1-4 years prior to the assault The Price of Violence

  6. Pre-assault characteristics of women DESCRIPTIVE STATISTICS *** p<0.01, ** p<0.05, * p<0.1 The Price of Violence

  7. Pre-assault characteristics of men DESCRIPTIVE STATISTICS *** p<0.01, ** p<0.05, * p<0.1 The Price of Violence

  8. Missing Data EVALUATION PROBLEM Di: Assault victimization Yi1: Potential outcome for unit i after assault Yi0: Potential outcome for unit i, not assaulted For individual i=1,...,N we observe Yi = Yi1 Di +(1- Di) Yi0 -> Never both potential outcomes! The Price of Violence

  9. Identifying Assumption 1: SUTVA EVALUATION PROBLEM Stable Unit Treatment Value Assumption: {Yi1, Yi0 } are functions of Di and i only. (Unobservable) effect of assault for individual i: Δi= Yi1-Yi0 Average effect of assault on the assaulted = ATT = E(Δ|D=1) = E(Y1|D=1) - E(Y0|D=1) The Price of Violence

  10. Identifying Assumption 2: CIA EVALUATION PROBLEM Weak Unconfoudedness: Y0 is independent of D conditional on X Propensity score: p(X) = P(D=1|X) Enough: Y0 indep of D | p(X) (Rosenbaum & Rubin, 1983) ATT(p(X)) = E(Y1|D=1,p(X)) - E(Y0|D=1,p(X)) = E(Y1|D=1,p(X)) - E(Y0|D=0,p(X)) The Price of Violence

  11. Identifying Assumption 3: Overlap EVALUATION PROBLEM For all assaulted, there are non-assaulted with the same characteristics as those of the assaulted: P(D=1|X)<1; X ATT=E( ATT(p(X)) ) The Price of Violence

  12. Matching: Basic Idea MATCHING Match assaulted to non-assaulted with similar characteristics (here: close values on p(X)) {k(1,i),..., k(M,i)} the M nearest matches for i Define: Ỹi1=Yi1|D=1 Ỹi0 =Yk(j,i)/M|D=0; j=1,...,M Estimator: ATTM=  (Ỹi1-Ỹi0)/N; 1,...,N The Price of Violence

  13. Matching In Practice MATCHING Risk set matching: p(Xt) (Li et. al., 2001) No future information, controls may become assaulted ATT now instead of possibly later: lower bound on ATT Parametric estimation: logit(p(Xit)) = βXit Separate estimations based on sex & employment Xt = {age t, years of schooling t, employmentt, disposable income t, days on sickness benefits and disability benefits t, hospital visits t, hospitalization for mental problem t, marital/cohabiting status t, recent separation from partner t, number of children, age of children, and custody} Nearest neighbor with replacement, 5 matches The Price of Violence

  14. How well can we estimate ATT? MATCHING Estimated propensity score: Bias of order Op(N-1/dim(X)) Bias correction -> Reduces bias to Op(N-1/2) Match values: Ỹi0 = jYk(j,i)/M|D=0; j=1,...,M Regression estimate: μ0(X)=E(Ỹ0|X) (matches) Bias corrected match value: Y_biask(j,i) = Yk(j,i) + μ0(Xi) - μ0(Xk(j,i)) (Abadie & Imbens, 2012) The Price of Violence

  15. Inference: V(ATTM) MATCHING Define: KM(i)= #copies of match i /M K´M(i)= #copies of match i /M2 V(ATTM) = i[ Di(Yi1 - Yi0 - ATT)2 + (1-Di)σ2( KM(i)2 - K´M(i) )2 ] σ2 = V(ATT) (Abadie et. al, 2004; Abadie & Imbens, 2006) The Price of Violence

  16. Assessing Assumptions MATCHING • Overlap 2) Unconfoundedness Check match quality Selection on observables only: Plausible? Sensitivity analysis The Price of Violence

  17. Match Quality: Women MATCHING *** p<0.01, ** p<0.05, * p<0.1 The Price of Violence

  18. Match Quality: Men MATCHING *** p<0.01, ** p<0.05, * p<0.1 The Price of Violence

  19. GRAPHICAL EVIDENCE Probability of employment, women The Price of Violence

  20. GRAPHICAL EVIDENCE Probability of employment, men The Price of Violence

  21. GRAPHICAL EVIDENCE Days on sickness insurance, women The Price of Violence

  22. GRAPHICAL EVIDENCE Days on sickness insurance, men The Price of Violence

  23. ESTIMATION RESULTS Short-term effects of assault on employment probability Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1 The Price of Violence

  24. ESTIMATION RESULTS Long term impact of assault on probability of employment Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1 The Price of Violence

  25. ESTIMATION RESULTS Short-term effects of assault on days on sickness insurance (Absolute numbers). Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1 The Price of Violence

  26. ESTIMATION RESULTS Long term impact of assault on sickness insurance uptake Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1 The Price of Violence

  27. Summing up... • Large selection and large causal effect of violence on both employment and sickness insurance uptake • Larger impact for employed than unemployed • Larger (absolute) impact for women than for men – but large effects for men as well! • Effects remain over time The Price of Violence

  28. Excerpt from p(Xt)-estimation The Price of Violence

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