AADAPT Workshop Latin America Brasilia, November 16-20, 2009 Non-Experimental Methods Florence Kondylis

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

 Find a plausible counterfactual  Every non-experimental method is associated with a set of assumptions  The stronger the assumption, the more doubtful our measure of the causal effect ▪ Question our assumptions ▪ Reality check, resort to common sense! 3

 Principal Objective ▪ Increase maize production  Intervention ▪ Fertilizer Vouchers distribution ▪ Non-random assignment  Target group ▪ Maize producers, land over 1 Ha & under 5 Ha  Main result indicator ▪ Maize yield 4

5 (+) Impact of the program (+) Impact of external factors

6 (+) BIASED Measure of the program impact “Before-After” doesn’t deliver results we can believe in!

7 « After » difference btw participants and non-participants « Before» difference btw participants and nonparticipants >> What’s the impact of our intervention?

Counterfactual: 2 Options 1. Non-participant maize yield after the intervention, accounting for the “before” difference between participants/nonparticipants (the initial gap between groups) 2. Participant maize yield before the intervention, accounting for the “before/after” difference for nonparticipants (the influence of external factors)  1 and 2 are equivalent 8

Underlying assumption: Without the intervention, maize yield for participants and non participants’ would have followed the same trend >> Graphic intuition coming…

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12 NP NP 2007 =0.8 Impact = (P P 2007 ) -(NP NP 2007 ) = 0.6 – 0.8 = -0.2 Impact = (P P 2007 ) -(NP NP 2007 ) = 0.6 – 0.8 = -0.2 P P 2007 =0.6

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

Impact=-0.2

 Negative Impact:  Very counter-intuitive: Increased input use should increase yield 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

>> Reject counterfactual assumption of same trends !

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18 NP 08 -NP 07 =0.2 Impact = (P P 2007 ) -(NP NP 2007 ) = 0.6 – 0.2 = Impact = (P P 2007 ) -(NP NP 2007 ) = 0.6 – 0.2 = + 0.4

Impact = +0.4

 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

>>Seems reasonable to accept counterfactual assumption of same trend ?!

 Assuming same trend is often problematic  No data to test the assumption  Even if trends are similar in the past… ▪ Where they always similar (or are we lucky)? ▪ More importantly, will they always be similar? ▪ Example: Other project intervenes in our nonparticipant villages…

 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, etc)

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 24

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

 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 ▪ Control Group: Non-participants similar enough to the participants >> We trim out a portion of our treatment group!

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

 Advantage of the matching method  Does not require randomization 28

 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… 29

 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! 30

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