Quasi Experimental Methods I Nethra Palaniswamy Development Strategy and Governance International Food Policy Research Institute

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 randomization is not feasible? >> Where it makes sense, resort to non-experimental methods

When does it make sense?  Can we find a plausible counterfactual?  Every non-experimental method is associated with a set of assumptions  Assumptions about plausible counterfactual  The stronger the assumptions, the more doubtful our measure of the causal effect  Question assumptions ▪ Are these assumptions valid?

Example: Funds for community infrastructure  Principal Objective ▪ Improving community infrastructure- primary schools Intervention ▪ Community grants ▪ Non-random assignment  Target group ▪ Communities with poor education infrastructure ▪ Communities with high poverty rates  Main result indicator ▪ Primary school enrolment

5 (+) Impact of the program (+) Impact of external factors Illustration: Funds for Community Infrastructure(1)

6 (+) BIASED Measure of the program impact Before-After comparisons

7 « After » Difference between participants and non-participants Before-After comparisons for participating and non-participating communities « Before» Difference between participants and non-participants >> What’s the impact of our intervention?

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

Difference-in-Differences Identification Strategy (2) Underlying assumption: Without the intervention, enrolments for participants and non participants’ would have followed the same trend >> Participating communities and non- partipating communities would have behaved in the same way on average, in the absence of the intervention

Data -- Example 1

NP NP 2007 =10.8 Impact = (P P 2007 ) -(NP NP 2007 ) = 10.6 – 10.8 = -0.2 Impact = (P P 2007 ) -(NP NP 2007 ) = 10.6 – 10.8 = -0.2 P P 2007 =10.6

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

Summary  Negative Impact:  Very counter-intuitive: Funding for building primary schools should not decrease enrolment rates once external factors are accounted for!  Assumption of same trend very strong  2 sets of communities groups had, in 2007, different pre- existing characteristics and different paths  Non-participating communities would have had slower increases in enrolment in the absence of funds for building primary schools ➤ Question the underlying assumption of same trend! ➤ When possible, test assumption of same trend with data from previous years

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

Data – Example 2

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

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

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

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 nonparticipating communities…

Caveats (2)  What to do? >> 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 (social cohesion, community participation)

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

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

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!

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

Conclusion (1)  Advantage of the matching method  Does not require randomization

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…

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