PAM 4380 february 16 - Outline: 2/16/2011 2.2 Empirical...

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Outline: 2/16/2011 2.2 Empirical methods Design-based econometrics Difference-in-difference Regression discontinuity Instrumental variables Readings Newhouse & McClellan 1998 Kenkel 1995 (next time) Homework #2 due Friday, 2/18
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Design-based econometrics Standard approach in health policy economics – Y i = α + ρ D i + β X i + η i – X i is a vector of control variables, i.e. observable characteristics of individual i that might be important to outcome Y i Want to estimate ρ = treatment effect – Main problem is selection bias: D i is endogenous, i.e. correlated with η i In experiment, D i is exogenous due to randomization Design-based econometrics: find a source of variation in D i that is sufficiently ‘like’ an experiment
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Difference-in-difference D-in-D design = “Before-and-after design with an untreated comparison group” Typical example: some states enact policy, others don’t First “difference” = difference over time in states that enacted policy (before-and-after) Second “difference” = difference (before-and-after comparison) in the control group of states that didn’t enact policy (untreated comparison group) Rules out general time trends, shocks that might be confounded with the ‘treatment’ of the policy Figure on next page from Angrist & Pischke
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Regression discontinuity Some discontinuity in assignment to an intervention Example 1: Maimonides’ Rule: class sizes ≤ 40 Child in a 5 th grade cohort of 40 students assigned to a 40- student class Child in a 5 th grade cohort of 41 students assigned to a 20- (or 21-) student class Source: Example 2: Assigned to treatment if poverty score ≤
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PAM 4380 february 16 - Outline: 2/16/2011 2.2 Empirical...

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