Global Workshop onDevelopment Impact Evaluation

in Finance and Private SectorRio de Janeiro, June 6-10, 2011

Mattea Stein

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

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Non-experimental methods

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!

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Example: Matching Grants 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

Before After0

2

4

6

8

10

12

14Control GroupTreatment Group

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(+) Impact of the program

(+) Impact of external factors

Illustration: Matching Grants - Randomization

Before After0

2

4

6

8

10

12

14Comparison GroupTreatment Group

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« After » difference btwnparticipants andnon-participants

Illustration: Matching Grants – Difference-in-difference

« Before» difference btwnparticipants and nonparticipants

>> What’s the impact of our intervention?

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Difference-in-Differences Identification Strategy (1)

Counterfactual: 2 Formulations that say the same thing

1.Non-participants’ sales after the intervention, accounting for the “before” difference between participants/nonparticipants (the initial gap between groups)

2.Participants’ sales before the intervention, accounting for the “before/after” difference for nonparticipants (the influence of external factors)

1 and 2 are equivalent

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Data – Example

Average sales(1000s)

2007 2008 Difference (2007-2008)

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

“After”-difference: P08-NP08=1.4“Before”-

difference:P07-NP07=1.0

Impact=0.4

Difference-in-DifferencesIdentification Strategy (2)

Underlying assumption:Without the intervention, sales for participants and non participants would have followed the same trend

>> Graphic intuition coming…

“After”-difference: P08-NP08=1.4

Impact=0.4

“Before”-difference:P07-NP07=1.0

Estimated Impact =0.4

True Impact=-0.3

Summary

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 !

Questioning the Assumption of same trend: Use pre-pr0gram data

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

2006 2007 20080

0.5

1

1.5

2

2.5

participantsnon-participants

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…

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)

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, matched participants and nonparticipants share the same observable characteristics (by construction)

Estimate the effect of our intervention by using difference-in-differences

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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

NonparticipantsParticipants

MatchedIndividuals

Wealth

Portion of treatmentgroup trimmed out

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Conclusion (1)

Advantage of the matching method Does not require randomization

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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…

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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

Thank you

Financial support from: Bank Netherlands Partnership Program (BNPP), Bovespa,

CVM, Gender Action Plan (GAP), Belgium & Luxemburg Poverty Reduction

Partnerships (BPRP/LPRP), Knowledge for Change Program (KCP), Russia Financial Literacy and Education Trust Fund (RTF), and the Trust Fund for Environmentally &

Socially Sustainable Development (TFESSD), is gratefully acknowledged.