1. Quasi Experimental Methods I
Non-Experimental Methods
Quasi Experimental Methods I
This presentation draws on previous presentations by Markus Goldstein, Leandre Bassole, Florence Kondylis and Alberto Martini

2. What we know so far
Aim: We want to isolate the causal effect of our interventions on our outcomes of interest
Use rigorous evaluation methods to answer our operational questions
Randomizing the assignment to treatment is the “gold standard” methodology (simple, precise, cheap)
What if we really, really (really??) cannot use it?!
>> Where it makes sense, resort to non-experimental methods

3. When does it make sense? Can we find a plausible counterfactual?
Natural experiment?
Every non-experimental method is associated with a set of assumptions
The stronger the assumptions, the more doubtful our measure of the causal effect
Question our assumptions
Reality check,
resort to common sense!

4. Example: Matching Grant Program
Principal Objective
Increase firm productivity and sales
Intervention
Matching grants distribution
Non-random assignment
Target group
SMEs with 1-10 employees
Main result indicator
Sales

5. Illustration: Matching Grant Program (1)
(+) Impact of the program
(+) Impact of external factors 5

6. Illustration: Matching Grant Program (2)
(+) BIASED Measure of the program impact
If no baseline data and no experimental design, can abandon the idea of measuring true impact (unless we have a natural experiment)
“Before-After” doesn’t deliver results we can believe in! 6

7. Illustration: Matching Grant Program (3)
« Before»
difference btwn
participants and nonparticipants
« After »
difference btwn
participants and
non-participants
>> What’s the impact of our intervention? 7

8. Difference-in-Differences Identification Strategy (1)
Counterfactual:
2 Formulations that say the same thing
Non-participants’ sales after the intervention, accounting for the “before” difference between participants/nonparticipants (the initial gap between groups)
Participants’ sales before the intervention, accounting for the “before/after” difference for nonparticipants (the influence of external factors)
1 and 2 are equivalent

9. Difference-in-Differences Identification Strategy (2)
Underlying assumption:
Without the intervention, sales for participants and non participants’ would have followed the same trend
>> Graphic intuition coming…

10. Data -- Example 1 1.3 1.9 0.6 1.4 0.8 0.7 0.5 -0.2 Average sales
2007
2008
Difference ( )
Participants (P)
1.3
1.9
0.6
Non-participants (NP)
1.4
0.8
Difference (P-NP)
0.7
0.5
-0.2

11. Data -- Example 1 1.3 1.9 0.6 1.4 0.8 0.7 0.5 -0.2 Average sales
2007
2008
Difference ( )
Participants (P)
1.3
1.9
0.6
Non-participants (NP)
1.4
0.8
Difference (P-NP)
0.7
0.5
-0.2

12. Impact = (P2008-P2007) -(NP2008-NP2007)
= 0.6 – 0.8 = -0.2
P2008-P2007=0.6
NP2008-NP2007=0.8

13. Impact = (P-NP)2008-(P-NP)2007
= = -0.2
P-NP2008=0.5
P-NP2007=0.7

14. Assumption of same trend: Graphic Implication
Impact=-0.2

15. Summary Negative Impact: Assumption of same trend very strong
Very counter-intuitive: Increased input use should not decrease sales once external factors are accounted for!
Assumption of same trend very strong
2 groups were, in 2007, producing at very different levels
Question the underlying assumption of same trend!
When possible, test assumption of same trend with data from previous years

16. Questioning the Assumption of same trend: Use pre-pr0gram data
>> Reject counterfactual assumption of same trends !

17. Data – Example 2 1.5 2.1 0.6 0.5 0.7 0.2 1.0 1.4 0.4 Average Sales
2007
2008
Difference ( )
Participants (P)
1.5
2.1
0.6
Non-participants (NP)
0.5
0.7
0.2
Difference (P-NP)
1.0
1.4
0.4

18. Impact = (P2008-P2007) -(NP2008-NP2007)
= 0.6 – 0.2 = + 0.4
NP08-NP07=0.2

19. Assumption of same trend: Graphic Implication
Impact = +0.4

20. Conclusion Positive Impact:
More intuitive
Is the assumption of same trend reasonable?
Still need to question the counterfactual assumption of same trends !
Use data from previous years

21. Questioning the Assumption of same trend: Use pre-pr0gram data
>>Seems reasonable to accept counterfactual
assumption of same trend ?!

22. Caveats (1) Assuming same trend is often problematic
No data to test the assumption
Even if trends are similar the previous year…
Where they always similar (or are we lucky)?
More importantly, will they always be similar?
Example: Other project intervenes in our nonparticipant firms…

23. Caveats (2) What to do? >> Be descriptive!
Check similarity in observable characteristics
If not similar along observables, chances are trends will differ in unpredictable ways
>> Still, we cannot check what we cannot see… And unobservable characteristics might matter more than observable (ability, motivation, patience, etc)

24. Matching Method + Difference-in-Differences (1)
Match participants with non-participants on the basis of observable characteristics
Counterfactual:
Matched comparison group
Each program participant is paired with one or more similar non-participant(s) based on observable characteristics
>> On average, participants and nonparticipants share the same observable characteristics (by construction)
Estimate the effect of our intervention by using difference-in-differences

25. Matching Method (2) Underlying counterfactual assumptions
After matching, there are no differences between participants and nonparticipants in terms of unobservable characteristics
AND/OR
Unobservable characteristics do not affect the assignment to the treatment, nor the outcomes of interest

26. How do we do it?
Design a control group by establishing close matches in terms of observable characteristics
Carefully select variables along which to match participants to their control group
So that we only retain
Treatment Group: Participants that could find a match
Comparison Group: Non-participants similar enough to the participants
>> We trim out a portion of our treatment group!

27. Implications In most cases, we cannot match everyone Example
Need to understand who is left out
Example
Matched
Individuals
Portion of treatment
group trimmed out
Nonparticipants
Participants
Score
Wealth

28. Conclusion (1) Advantage of the matching method
Does not require randomization

29. Conclusion (2) Disadvantages:
Underlying counterfactual assumption is not plausible in all contexts, hard to test
Use common sense, be descriptive
Requires very high quality data:
Need to control for all factors that influence program placement/outcome of choice
Requires significantly large sample size to generate comparison group
Cannot always match everyone…

30. Summary
Randomized-Controlled-Trials require minimal assumptions and procure intuitive estimates (sample means!)
Non-experimental methods require assumptions that must be carefully tested
More data-intensive
Not always testable
Get creative:
Mix-and-match types of methods!
Address relevant questions with relevant techniques