DIME – FRAGILE STATESDUBAI, MAY 31 – JUNE 4

Why Use Randomized Evaluation?

Isabel Beltran, World Bank

Fundamental Question

What is the effect of a program or intervention?

How can vulnerable groups partake in the state and peace building process?

What political and social accountability mechanisms are most effective in a fragile state?

What measures secure stability and reduce ethnic conflict at the local level?

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Objective

To Identify the causal effect of an intervention Identify the impact of the program from other

factors

Need to find out what would have happened without the program Cannot observe the same person with and

without the program at the same point of time Create a valid counterfactual

Correlation is not causation

Higher profits

Credit Use

OR ?

2)

?

1) Higher profits

Business Skills

Credit

Question: Does providing credit increase firm profits?

Suppose we observe that firms with more credit also earn higher profits.

2007 20090

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Treatment GroupTreatment Group

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(+6) increase in gross operating margin

Illustration: Credit Program (Before-After)

A credit program was offered in 2008.

Why did operating margin increase?

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Motivation

Hard to distinguish causation from correlation by analyzing existing (retrospective) data However complex, statistics can only see that X moves

with Y Hard to correct for unobserved characteristics, like

motivation/ability May be very important- also affect outcomes of interest

Selection bias a major issue for impact evaluation Projects started at specific times and places for particular

reasons Participants may be selected or self-select into programs People who have access to credit are likely to be very

different from the average entrepreneur, looking at their profits will give you a misleading impression of the benefits of credit

Before After0

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14Control GroupTreatment Group

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(+4) Impact of the program

(+2) Impact of other (external) factors

Illustration: Credit Program (Valid Counterfactual)

* Macroeconomic environment affects control group* Program impact easily identified

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Experimental Design

All those in the study have the same chance of being in the treatment or comparison group

By design, treatment and comparison have the same characteristics (observed and unobserved), on average Only difference is treatment

Large sample all characteristics average out

Unbiased impact estimates

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Options for Randomization Lottery (0nly some receive)

Lottery to receive new loans, credit for community Random phase-in (everyone gets it

eventually) Some groups or individuals get credit each year

Variation in treatment Some get matching grant, others get credit, others

get business development services etc Encouragement design

Some farmers get home visit to explain loan product, others do not

Lottery among the qualified

Must receive the program

Not suitable for the program

Randomize who gets the program

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Opportunities for Randomization

Budget constraint prevents full coverage Random assignment (lottery) is fair and

transparent Limited implementation capacity

Phase-in gives all the same chance to go first

No evidence on which alternative is best Random assignment to alternatives

with equal ex ante chance of success

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Opportunities for Randomization

Take up of existing program is not complete Provide information or incentive for some to

sign up- Randomize encouragement Pilot a new program

Good opportunity to test design before scaling up

Operational changes to ongoing programs Good opportunity to test changes before

scaling them up

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Different levels you can randomize at

Individual/owner/firm Business Association Village level School level

Women’s association

Youth groups Regulatory

jurisdiction/ administrative district

Group or individual randomization?

If a program impacts a whole group-- usually randomize whole community to treatment or comparison

Easier to get big enough sample if randomize individuals

Individual randomization Group randomization

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Unit of Randomization

Randomizing at higher level sometimes necessary: Political constraints on differential treatment within

community Practical constraints—confusing to implement

different versions Spillover effects may require higher level

randomization

Randomizing at group level requires many groups because of within community correlation

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Elements of an experimental design

Random assignmentTreatment Group Control Group• Participants Non-participants

Evaluation sample

Potential participantsTailors Furniture manufacturers

Target population

SMEs

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External and Internal Validity (1)

External validity The evaluation sample is representative of the

total population The results in the sample represent the results

in the population We can apply the lessons to the whole population

Internal validity The intervention and comparison groups are

truly comparable estimated effect of the intervention/program

on the evaluated population reflects the real impact on that population

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External and Internal Validity (2)

An evaluation can have internal validity without external validity

Example: A randomized evaluation of encouraging informal firms to register in urban areas may not tell us much about impact of a similar program in rural areas

An evaluation without internal validity, can’t have external validity

If you don’t know whether a program works in one place, then you have learnt nothing about whether it works elsewhere.

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Internal & external validity

Random Sample- Randomization

Randomization

National Population National Population

Representative Sample of National

Population

Representative Sample of National

Population

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Internal validity

Stratification

Randomization

PopulationPopulation

Population stratumPopulation stratumSamples of Population

StratumSamples of Population

Stratum

Example: Evaluating a program that targets women

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Representative but biased: useless

National Population National Population

Non-random assignment

USELESS!

Non-random assignment

USELESS!

Randomization

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Efficacy & Effectiveness

Efficacy Proof of concept Smaller scale Pilot in ideal conditions

Effectiveness At scale Prevailing implementation arrangements --

“real life”

Higher or lower impact? Higher or lower costs?

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Advantages of “experiments”

Clear and precise causal impactRelative to other methods

Provide correct estimates Much easier to analyze- Difference in

averages Easier to explain More convincing to policymakers Methodologically uncontroversial

Randomly assigning machines within a plant to receive regular maintenance

Machines do NOT Raise ethical or practical concerns

about randomization Fail to comply with Treatment Find a better Treatment Move away—so lost to measurement Refuse to answer questionnaires

Human beings can be a little more challenging!

What if there are constraints on randomization?

Some interventions can’t be assigned randomly

Partial take up or demand-driven interventions: Randomly promote the program to some Participants make their own choices about

adoption Perhaps there is contamination- for

instance, if some in the control group take-up treatment

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Randomly Assigned Marketing(Encouragement Design)

Those who get receive marketing treatment are more likely to enroll

But who got marketing was determined randomly, so not correlated with other observables/non-observables Compare average outcomes of two groups:

promoted/not promoted Effect of offering the encouragement (Intent-To-

Treat) Effect of the intervention on the complier

population (Local Average Treatment Effect)▪ LATE= ITT/proportion of those who took it up

RandomizationAssigned to treatment

Assigned to control

Difference Impact: Average treatment effect on the treated

Non-treated

Treated

Proportion treated

100% 0% 100%Impact of assignment

100%

Mean outcome

103 80 23Intent-to-treat estimate

23/100%=23Average treatment on the treated

Random encouragementRandomlyEncouraged

Not encouraged

Difference Impact: Average treatment effect on compliers

Non-treated(did not take up program)

Treated(did take up program)

Proportion treated

70% 30% 40%Impact of encouragement

100%

Outcome 100 92 8Intent-to-treat estimate

8/40%=20Local average treatment effect

Common pitfalls to avoid

Calculating sample size incorrectly Randomizing one district to treatment and

one district to control and calculating sample size on number of people you interview

Collecting data in treatment and control differently

Counting those assigned to treatment who do not take up program as control—don’t undo your randomization!!

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When is it really not possible?

The treatment already assigned and announced

and no possibility for expansion of treatment

The program is over (retrospective)Universal take up alreadyProgram is national and non

excludable Freedom of the press, exchange rate

policy(sometimes some components can be

randomized)Sample size is too small to make it

worth it

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Thank You