1. Differences-in-Differences
November 10, 2009
Erick Gong
Thanks to Null & Miguel

2. Agenda Class Scheduling Diff-in-Diff (Math & Graphs) Case Study
STATA Help

3. Class Scheduling Nov 10: Diff-in-Diff
Nov 17: Power Calculations & Guest Speaker
Nov 24: Class poll: Who will be here?
Dec 1: Review & Presentations
Class Poll: Who will be presenting their research proposals?

4. The Big Picture
What is this class really about, anyway?

5. The Big Picture
What is this class really about, anyway?
Causality

6. The Big Picture What is this class really about, anyway?
Causality
What is our biggest problem?

7. The Big Picture What is this class really about, anyway?
Causality
What is our biggest problem?
Omitted variable bias

8. Omitted Variable Bias The actual cause is unobserved
e.g. higher wages for educated actually caused by motivation, not schooling
Happens when people get to choose their own level of the “treatment” (broadly construed)
Selection bias
Non-random program placement
Because of someone else’s choice, “control” isn’t a good counterfactual for treated

9. Math Review
(blackboard)

10. Math Review (1) Yi = a + bTi + cXi + ei
for those of you looking at these slides later, here’s what we just wrote down:
(1) Yi = a + bTi + cXi + ei
(2) E(Yi | Ti=1) – E(Yi | Ti=0)
= [a + b + cE(Xi | Ti=1) + E(ei | Ti=1)]
– [a cE(Xi | Ti=0) + E(ei | Ti=0)]
= b c [E(Xi | Ti=1) – E(Xi | Ti=0)]
True effect “Omitted variable/selection bias” term

11. What if we had data from before the program?
What if we estimated this equation using data from before the program?
(1) Yi = a + bTi + cXi + ei
Specifically, what would our estimate of b be?

12. What if we had data from before the program?
What if we estimated this equation using data from before the program?
(1) Yi = a + bTi + cXi + ei
(2) E(Yi0| Ti1=1) – E(Yi0| Ti1=0)
= [a cE(Xi0 | Ti1=1) + E(ei0| Ti1=1)]
– [a cE(Xi0| Ti1=0) + E(ei0| Ti1=0)]
= c [E(Xi | Ti=1) – E(Xi | Ti=0)]
“Omitted variable/selection bias” term
ALL THAT’S LEFT IS THE PROBLEMATIC TERM – HOW COULD THIS BE HELPFUL TO US?

13. Differences-in-Differences (just what it sounds like)
Use two periods of data
add second subscript to denote time
= {E(Yi1 | Ti1=1) – E(Yi1 | Ti1=0)} (difference btwn T&C, post)
– {E(Yi0 | Ti1=1) – E(Yi0 | Ti1=0)} – (difference btwn T&C, pre)
= b + c [E(Xi1 | Ti1=1) – E(Xi1 | Ti1=0)]
– c [E(Xi0 | Ti1=1) – E(Xi0 | Ti1=0)]

14. Differences-in-Differences (just what it sounds like)
Use two periods of data
add second subscript to denote time
= {E(Yi1 | Ti1=1) – E(Yi1 | Ti1=0)} (difference btwn T&C, post)
– {E(Yi0 | Ti1=1) – E(Yi0 | Ti1=0)} – (difference btwn T&C, pre)
= b + c [E(Xi1 | Ti1=1) – E(Xi1 | Ti1=0)]
– c [E(Xi0 | Ti1=1) – E(Xi0 | Ti1=0)]
= b YAY!
Assume differences between X don’t change over time.

15. Differences-in-Differences, Graphically
Treatment
Control
Pre
Post

16. Differences-in-Differences, Graphically
Effect of program using only pre- & post- data from T group (ignoring general time trend).
Pre
Post

17. Differences-in-Differences, Graphically
Effect of program using only T & C comparison from post-intervention (ignoring pre-existing differences between T & C groups).
Pre
Post

18. Differences-in-Differences, Graphically
Pre
Post

19. Differences-in-Differences, Graphically
Effect of program difference-in-difference (taking into account pre-existing differences between T & C and general time trend).
Pre
Post

20. Identifying Assumption
Whatever happened to the control group over time is what would have happened to the treatment group in the absence of the program.
Effect of program difference-in-difference (taking into account pre-existing differences between T & C and general time trend).
Pre
Post

21. Graphing Exercise Form Groups of 3-4 4 Programs
Pre-Post Treatment Effect
Take the difference of post-treatment outcome vs. pre-treatment outcome
Post-intervention (Treatment vs. Control) Comparison
Circle what you think is pre-post effect and post-intervention treat vs. control effect
Ask group volunteers

22. Uses of Diff-in-Diff Simple two-period, two-group comparison
very useful in combination with other methods

23. Uses of Diff-in-Diff Simple two-period, two-group comparison
very useful in combination with other methods
Randomization
Regression Discontinuity
Matching (propensity score)

24. Uses of Diff-in-Diff Simple two-period, two-group comparison
very useful in combination with other methods
Randomization
Regression Discontinuity
Matching (propensity score)
Can also do much more complicated “cohort” analysis, comparing many groups over many time periods

25. The (Simple) Regression
Yi,t = a + bTreati,t+ cPosti,t + d(Treati,tPosti,t )+ ei,t
Treati,t is a binary indicator (“turns on” from 0 to 1) for being in the treatment group
Posti,t is a binary indicator for the period after treatment
and Treati,tPosti,t is the interaction (product)
Interpretation of a, b, c, d is “holding all else constant”

26. Putting Graph & Regression Together
Yi,t = a + bTreati,t+ cPosti,t + d(Treati,tPosti,t )+ ei,t
d is the causal effect of treatment
a + b + c + d
a + b
a + c a
Pre
Post

27. Putting Graph & Regression Together
Yi,t = a + bTreati,t+ cPosti,t + d(Treati,tPosti,t )+ ei,t
a + b + c + d
Single Diff 2= (a+b+c+d)-(a+c) = (b+d)
a + b
Single Diff 1= (a+b)-(a)=b
a + c a
Pre
Post

28. Putting Graph & Regression Together
Yi,t = a + bTreati,t+ cPosti,t + d(Treati,tPosti,t )+ ei,t
Diff-in-Diff=(Single Diff 2-Single Diff 1)=(b+d)-b=d
a + b + c + d
Single Diff 2 = (a+b+c+d)-(a+c) = (b+d)
a + b
Single Diff 1= (a+b)-(a)=b
a + c a
Pre
Post

29. Cohort Analysis
When you’ve got richer data, it’s not as easy to draw the picture or write the equations
cross-section (lots of individuals at one point in time)
time-series (one individual over lots of time)
repeated cross-section (lots of individuals over several times)
 panel (lots of individuals, multiple times for each) 
Basically, control for each time period and each “group” (fixed effects) – the coefficient on the treatment dummy is the effect you’re trying to estimate

30. DiD Data Requirements Either repeated cross-section or panel
Treatment can’t happen for everyone at the same time
If you believe the identifying assumption, then you can analyze policies ex post
Let’s us tackle really big questions that we’re unlikely to be able to randomize

31. Malaria Eradication in the Americas (Bleakley 2007)
Question: What is the effect of malaria on economic development?
Data: Malaria Eradication in United States South (1920’s) Brazil, Colombia, Mexico (1950’s)
Diff-in-Diff: Use birth cohorts (old people vs. young people) & (regions with lots of malaria vs. little malaria)
Idea: Young Cohort X Region w/malaria
Result: This group higher income & literacy

32. What’s the intuition
Areas with high pre-treatment malaria will most benefit from malaria eradication
Young people living in these areas will benefit most (older people might have partial immunity)
Comparison Group: young people living in low pre-treatment malaria areas (malaria eradication will have little effect here)

33. Robustness Checks
If possible, use data on multiple pre-program periods to show that difference between treated & control is stable
Not necessary for trends to be parallel, just to know function for each
If possible, use data on multiple post-program periods to show that unusual difference between treated & control occurs only concurrent with program
Alternatively, use data on multiple indicators to show that response to program is only manifest for those we expect it to be (e.g. the diff-in-diff estimate of the impact of ITN distribution on diarrhea should be zero)

34. Intermission
Come back if intro to PS4
STATA tips

35. Effect of 2ndary School Construction in Tanzania
Villages
“Treatment Villages” got 2ndary schools
“Control Villages” didn’t
Who benefits from 2ndary schools?
Young People benefit
Older people out of school shouldn’t benefit
Effect: (Young People X Treatment Villages)