双重差分模型幻灯片+-+difference+in+differences+models
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• Yit = β0 + β3 TitAit + ui + λt + εit
百度文库
18
What is nice about the model
• 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
– ui and TitAit – λt and TitAit
19
• Group effects
– Capture differences across groups that are constant over time
3
Y True effect = Yt2-Yt1 Estimated effect = Yb-Ya
Yt1
Ya Yb Yt2
t1
ti
t2
time
4
• Intervention occurs at time period t1 • True effect of law
– Ya – Yb
• Only have data at t1 and t2
6
Three different presentations
• Tabular • Graphical • Regression equation
7
Difference in Difference
Before Change
Group 1 (Treat) Group 2 (Control) Difference Yt1 Yc1
– If using time series, estimate Yt1 – Yt2
• Solution?
5
Difference in difference models
• Basic two-way fixed effects model
– Cross section and time fixed effects
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)
After Change
Yt2 Yc2
Difference ΔYt = Yt2-Yt1 ΔYc =Yc2-Yc1 ΔΔY ΔYt – ΔYc
8
Y Treatment effect= (Yt2-Yt1) – (Yc2-Yc1) Yc1
Yt1
Yc2
Yt2
control treatment t1 t2
time
• 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
9
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)
Difference in Difference Models
Bill Evans Spring 2008
1
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
• Many periods • Intervention will occur in a group of states but at a variety of times
17
• ui is a state effect • vt is a complete set of year (time) effects • Analysis of covariance model
• Problem:
– given progressive nature of benefits, replaced wages reveal a lot about the workers – Replacement rates higher in higher wage states
23
• • • • •
10
Y
Yc1
Treatment effect= (Yt2-Yt1) – (Yc2-Yc1)
Yc2 Yt1
control Yt2 treatment t1 t2 Treatment Effect
time
11
• In contrast, what is key is that the time trends in the absence of the intervention are the same in both groups • If the intervention occurs in an area with a different trend, will under/over state the treatment effect • In this example, suppose intervention occurs in area with faster falling Y
24
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)
• Year effects
– Capture differences over time that are common to all groups
20
Meyer et al.
• Workers‟ compensation
– State run insurance program – Compensate workers for medical expenses and lost work due to on the job accident
12
Y
Estimated treatment Yc1
Yt1
Yc2
True treatment effect
control
Yt2 treatment t1 t2
True Treatment Effect
time
13
Basic Econometric Model
• Data varies by
– state (i) – time (t) – Outcome is Yit
After Change
Difference
β0+ β1+ β2+ β3 ΔYt = β2 + β3 β0 + β2 ΔYc = β2 ΔΔY = β3
16
More general model
• Data varies by
– state (i) – time (t) – Outcome is Yit
• Yit = β0 + β1Hit + β2Ait + β3AitHit + β4Xit‟ + εit • Diff-in-diff estimate is β3
28
29
Questions to ask?
• What parameter is identified by the quasiexperiment? Is this an economically meaningful parameter? • What assumptions must be true in order for the model to provide and unbiased estimate of β3? • Do the authors provide any evidence supporting these assumptions?
• Yit = β0 + β1Tit + β2Ait + β3TitAit + εit
15
Yit = β0 + β1Tit + β2Ait + β3TitAit + εit
Before Change
Group 1 (Treat) Group 2 (Control) Difference β0 + β1 β0
• Premiums
– Paid by firms – Function of previous claims and wages paid
• Benefits -- % of income w/ cap
21
• Typical benefits schedule
– Min( pY,C) – P=percent replacement – Y = earnings – C = cap – e.g., 65% of earnings up to $400/month
22
• Concern:
– Moral hazard. Benefits will discourage return to work
• Empirical question: duration/benefits gradient • Previous estimates
– Regress duration (y) on replaced wages (x)
• Only two periods • Intervention will occur in a group of observations (e.g. states, firms, etc.)
14
• 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
• Compare change in duration of spell before and after change for these two groups
25
26
27
Model
• Yit = duration of spell on WC • Ait = period after benefits hike • Hit = high earnings group (Income>E3)
2
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
百度文库
18
What is nice about the model
• 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
– ui and TitAit – λt and TitAit
19
• Group effects
– Capture differences across groups that are constant over time
3
Y True effect = Yt2-Yt1 Estimated effect = Yb-Ya
Yt1
Ya Yb Yt2
t1
ti
t2
time
4
• Intervention occurs at time period t1 • True effect of law
– Ya – Yb
• Only have data at t1 and t2
6
Three different presentations
• Tabular • Graphical • Regression equation
7
Difference in Difference
Before Change
Group 1 (Treat) Group 2 (Control) Difference Yt1 Yc1
– If using time series, estimate Yt1 – Yt2
• Solution?
5
Difference in difference models
• Basic two-way fixed effects model
– Cross section and time fixed effects
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)
After Change
Yt2 Yc2
Difference ΔYt = Yt2-Yt1 ΔYc =Yc2-Yc1 ΔΔY ΔYt – ΔYc
8
Y Treatment effect= (Yt2-Yt1) – (Yc2-Yc1) Yc1
Yt1
Yc2
Yt2
control treatment t1 t2
time
• 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
9
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)
Difference in Difference Models
Bill Evans Spring 2008
1
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
• Many periods • Intervention will occur in a group of states but at a variety of times
17
• ui is a state effect • vt is a complete set of year (time) effects • Analysis of covariance model
• Problem:
– given progressive nature of benefits, replaced wages reveal a lot about the workers – Replacement rates higher in higher wage states
23
• • • • •
10
Y
Yc1
Treatment effect= (Yt2-Yt1) – (Yc2-Yc1)
Yc2 Yt1
control Yt2 treatment t1 t2 Treatment Effect
time
11
• In contrast, what is key is that the time trends in the absence of the intervention are the same in both groups • If the intervention occurs in an area with a different trend, will under/over state the treatment effect • In this example, suppose intervention occurs in area with faster falling Y
24
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)
• Year effects
– Capture differences over time that are common to all groups
20
Meyer et al.
• Workers‟ compensation
– State run insurance program – Compensate workers for medical expenses and lost work due to on the job accident
12
Y
Estimated treatment Yc1
Yt1
Yc2
True treatment effect
control
Yt2 treatment t1 t2
True Treatment Effect
time
13
Basic Econometric Model
• Data varies by
– state (i) – time (t) – Outcome is Yit
After Change
Difference
β0+ β1+ β2+ β3 ΔYt = β2 + β3 β0 + β2 ΔYc = β2 ΔΔY = β3
16
More general model
• Data varies by
– state (i) – time (t) – Outcome is Yit
• Yit = β0 + β1Hit + β2Ait + β3AitHit + β4Xit‟ + εit • Diff-in-diff estimate is β3
28
29
Questions to ask?
• What parameter is identified by the quasiexperiment? Is this an economically meaningful parameter? • What assumptions must be true in order for the model to provide and unbiased estimate of β3? • Do the authors provide any evidence supporting these assumptions?
• Yit = β0 + β1Tit + β2Ait + β3TitAit + εit
15
Yit = β0 + β1Tit + β2Ait + β3TitAit + εit
Before Change
Group 1 (Treat) Group 2 (Control) Difference β0 + β1 β0
• Premiums
– Paid by firms – Function of previous claims and wages paid
• Benefits -- % of income w/ cap
21
• Typical benefits schedule
– Min( pY,C) – P=percent replacement – Y = earnings – C = cap – e.g., 65% of earnings up to $400/month
22
• Concern:
– Moral hazard. Benefits will discourage return to work
• Empirical question: duration/benefits gradient • Previous estimates
– Regress duration (y) on replaced wages (x)
• Only two periods • Intervention will occur in a group of observations (e.g. states, firms, etc.)
14
• 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
• Compare change in duration of spell before and after change for these two groups
25
26
27
Model
• Yit = duration of spell on WC • Ait = period after benefits hike • Hit = high earnings group (Income>E3)
2
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