双重差分模型difference in differences models

• Use time series of untreated group to establish what would have occurred in the absence of the intervention • Key concept: can control for the fact that the intervention is more likely in some types of states
• Only two periods • Intervention will occur in a group of observations (e.g. states, firms, etc.)
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• Three key variables
– Tit =1 if obs i belongs in the state that will eventually be treated – Ait =1 in the periods when treatment occurs – TitAit -- interaction term, treatment states after the intervention
Difference in Difference Models
Bill Evans Spring 2008
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Difference in difference models
• Maybe the most popular identification strategy in applied work today • Attempts to mimic random assignment with treatment and “comparison” sample • Application of two-way fixed effects model
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• ui is a state effect • vt is a complete set of year (time) effects • Analysis of covariance model • Yit = β0 + β3 TitAit + ui + λt + εit
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What is nice about the model
– ui and TitAit – λt and TitAit
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• Group effects
– Capture differences across groups that are constant over time
• Year effects
– Capture differences over time that are common to all groups
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Y Treatment effect= (Yt2-Yt1) – (Yc2-Yc1) Yc1 Yt1
Yc2
Yt2
control treatment t1 t2
time
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Key Assumption
• Control group identifies the time path of outcomes that would have happened in the absence of the treatment • In this example, Y falls by Yc2-Yc1 even without the intervention • Note that underlying ‘levels’ of outcomes are not important (return to this in the regression equation)
Yi = Xiβ + αRi + εi Y (duration) R (replacement rate) Expect α > 0 Expect Cov(Ri, εi)
– Higher wage workers have lower R and higher duration (understate) – Higher wage states have longer duration and longer R (overstate)
• Suppose interventions are not random but systematic
– Occur in states with higher or lower average Y – Occur in time periods with different Y’s

• This is captured by the inclusion of the state/time effects – allows covariance between
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Meyer et al.
• Workers’ compensation
– State run insurance program – Compensate workers for medical expenses and lost work due to on the job accident
• Premiums
– Paid by firms – Function of previous claims and wages paid
• Benefits -- % of income w/ cap
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• Typical benefits schedule
– Min( pY,C) – P=percent replacement – Y = earnings – C = cap – e.g., 65% of earnings up to $400/month
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Questions to ask?
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Three different presentations
• Tabular • Graphical • Regression equation
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Difference in Difference
Before Change Group 1 (Treat) Group 2 (Control) Difference Yt1 Yc1 After Change Yt2 Yc2 Difference ΔY t = Yt2-Yt1 ΔY c =Yc2-Yc1 ΔΔY ΔY t – ΔY c
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Solution
• Quasi experiment in KY and MI • Increased the earnings cap
– Increased benefit for high-wage workers
• (Treatment)
– Did nothing to those already below original cap (comparison)
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• Concern:
– Moral hazard. Benefits will discourage return to work
• Empirical question: duration/benefits gradient • Previous estimates
– Regress duration (y) on replaced wages (x)
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Problem set up
• Cross-sectional and time series data • One group is ‘treated’ with intervention • Have pre-post data for group receiving intervention • Can examine time-series changes but, unsure how much of the change is due to secular changes
– If using time series, estimate Yt1 – Yt2
• Solution?
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Difference in difference models
• Basic two-way fixed effects model
– Cross section and time fixed effects
• Compare change in duration of spell before and after change for these two groups
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Model
• Yit = duration of spell on WC • Ait = period after benefits hike • Hit = high earnings group (Income>E3) • Yit = β0 + β1Hit + β2Ait + β3AitHit + β4Xit’ + εit • Diff-in-diff estimate is β3
• Yit = β0 + β1Tit + β2Ait + β3TitAit + εit
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Yit = β0 + β1Tit + β2Ait + β3TitAit + εit
Before Change Group 1 (Treat) Group 2 (Control) Difference β0+ β1 β0 After Change Difference
• Problem:
– given progressive nature of benefits, replaced wages reveal a lot about the workers – Replacement rates higher in higher wage states