Quasi Experimental Methods I

Florence Kondylis Non-Experimental Methods Quasi Experimental Methods I

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

Objective • 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!

Example: Fertilizer Voucher Program • 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

(+) Impact of the program Illustration: Fertilizer Voucher Program (1) (+) Impact of externalfactors 5

Illustration: Fertilizer Voucher Program (2) (+) BIASED Measure of the program impact “Before-After” doesn’t deliver results we can believe in! 6

Illustration: Fertilizer Voucher Program (3) « Before» differencebtw participants and nonparticipants « After » differencebtw participants and non-participants >> What’s the impact of our intervention? 7

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

Difference-in-DifferencesIdentification Strategy (2) Underlying assumption: Without the intervention, maize yield for participants and non participants’ would have followed the same trend >> Graphic intuition coming…

Data -- Example 1

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

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

Assumption of same trend: Graphic Implication Impact=-0.2

Summary • 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

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

Data – Example 2

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

Assumption of same trend: Graphic Implication Impact = +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 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…

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 mightmatter more than observable (ability, motivation, etc)

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 • Control 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 Matched Individuals Portion of treatment group trimmed out Nonparticipants Participants Score Wealth

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!

Thank You

Quasi Experimental Methods I