4 observations with income0 were dropped hhkids

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                                             (4 observations with income=0 were dropped)                     HHKIDS  =  children under age 16 in the household = 1; otherwise = 0                    EDUC  =  years of schooling                     AGE  =  age in years                    MARRIED  =  marital status                    EDUC  =  years of education ™  53/61
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Part 13: Endogeneity A study of moral hazard Riphahn, Wambach, Million: “Incentive Effects in the Demand for Healthcare” Journal of Applied Econometrics, 2003 ™  54/61
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Part 13: Endogeneity Evidence of Moral Hazard? ™  55/61
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Part 13: Endogeneity Regression Study ™  56/61
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Part 13: Endogeneity Endogenous Dummy Variable p Doctor Visits = f(Age, Educ, Health, Presence of Insurance, Other unobservables ) p Insurance = f(Expected Doctor Visits, Other unobservables ) ™  57/61
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Part 13: Endogeneity Approaches p (Parametric) Control Function: Build a structural model for the two variables (Heckman) p (Semiparametric) Instrumental Variable: Create an instrumental variable for the dummy variable (Barnow/Cain/ Goldberger, Angrist, Current generation of researchers) p (?) Propensity Score Matching (Heckman et al., Becker/Ichino, Many recent researchers) ™  58/61
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Part 13: Endogeneity Heckman’s Control Function Approach p Y = + δT + E[ε|T] + {ε - E[ε|T]} p λ = E[ε|T] , computed from a model for whether T = 0 or 1 Magnitude = 11.1200 is nonsensical in this context. ™  59/61
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Part 13: Endogeneity Instrumental Variable Approach Construct a prediction for T using only the exogenous information Use 2SLS using this instrumental variable. Magnitude = 23.9012 is also nonsensical in this context. ™  60/61
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Part 13: Endogeneity Propensity Score Matching p Create a model for T that produces probabilities for T=1: “Propensity Scores” p Find people with the same propensity score – some with T=1, some with T=0 p Compare number of doctor visits of those with T=1 to those with T=0.   61/61
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