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![Page 1: Randomized Evaluations: Applications Kenny Ajayi September 22, 2008 Global Poverty and Impact Evaluation.](https://reader035.fdocuments.net/reader035/viewer/2022062523/5a4d1ae77f8b9ab059979848/html5/thumbnails/1.jpg)
Randomized Evaluations:Applications
Kenny AjayiSeptember 22, 2008
Global Poverty and Impact Evaluation
![Page 2: Randomized Evaluations: Applications Kenny Ajayi September 22, 2008 Global Poverty and Impact Evaluation.](https://reader035.fdocuments.net/reader035/viewer/2022062523/5a4d1ae77f8b9ab059979848/html5/thumbnails/2.jpg)
Review
Causation and Correlation (5 factors) X → Y Y → X X → Y and Y → X Z → X and Z → Y
X ‽ Y
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Review
Causation and Correlation (5 factors) X → Y causality Y → X reverse causality X → Y and Y → X simultaneity Z → X and Z → Y omitted variables /
confounding X ‽ Y spurious correlation
Reduced-form relationship between X and Y
![Page 4: Randomized Evaluations: Applications Kenny Ajayi September 22, 2008 Global Poverty and Impact Evaluation.](https://reader035.fdocuments.net/reader035/viewer/2022062523/5a4d1ae77f8b9ab059979848/html5/thumbnails/4.jpg)
Review
Exogenous
![Page 5: Randomized Evaluations: Applications Kenny Ajayi September 22, 2008 Global Poverty and Impact Evaluation.](https://reader035.fdocuments.net/reader035/viewer/2022062523/5a4d1ae77f8b9ab059979848/html5/thumbnails/5.jpg)
Review
Exogenous
Endogenous
![Page 6: Randomized Evaluations: Applications Kenny Ajayi September 22, 2008 Global Poverty and Impact Evaluation.](https://reader035.fdocuments.net/reader035/viewer/2022062523/5a4d1ae77f8b9ab059979848/html5/thumbnails/6.jpg)
Review
Exogenous
Endogenous
External Validity
![Page 7: Randomized Evaluations: Applications Kenny Ajayi September 22, 2008 Global Poverty and Impact Evaluation.](https://reader035.fdocuments.net/reader035/viewer/2022062523/5a4d1ae77f8b9ab059979848/html5/thumbnails/7.jpg)
Review
Exogenous
Endogenous
External Validity
Natural Experiment
![Page 8: Randomized Evaluations: Applications Kenny Ajayi September 22, 2008 Global Poverty and Impact Evaluation.](https://reader035.fdocuments.net/reader035/viewer/2022062523/5a4d1ae77f8b9ab059979848/html5/thumbnails/8.jpg)
Practical Applications
Noncompliance
Cost Benefit Analysis
Theory and Mechanisms
![Page 9: Randomized Evaluations: Applications Kenny Ajayi September 22, 2008 Global Poverty and Impact Evaluation.](https://reader035.fdocuments.net/reader035/viewer/2022062523/5a4d1ae77f8b9ab059979848/html5/thumbnails/9.jpg)
Kling, Liebman and Katz (2007)
Do neighborhoods affect their residents?
![Page 10: Randomized Evaluations: Applications Kenny Ajayi September 22, 2008 Global Poverty and Impact Evaluation.](https://reader035.fdocuments.net/reader035/viewer/2022062523/5a4d1ae77f8b9ab059979848/html5/thumbnails/10.jpg)
Do neighborhoods affect their residents?
Positively
![Page 11: Randomized Evaluations: Applications Kenny Ajayi September 22, 2008 Global Poverty and Impact Evaluation.](https://reader035.fdocuments.net/reader035/viewer/2022062523/5a4d1ae77f8b9ab059979848/html5/thumbnails/11.jpg)
Do neighborhoods affect their residents?
Positively Social connections, role models, security, community
resources
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Do neighborhoods affect their residents?
Positively Social connections, role models, security, community
resources Negatively
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Do neighborhoods affect their residents?
Positively Social connections, role models, security, community
resources Negatively
Discrimination, competition with advantaged peers
![Page 14: Randomized Evaluations: Applications Kenny Ajayi September 22, 2008 Global Poverty and Impact Evaluation.](https://reader035.fdocuments.net/reader035/viewer/2022062523/5a4d1ae77f8b9ab059979848/html5/thumbnails/14.jpg)
Do neighborhoods affect their residents?
Positively Social connections, role models, security, community
resources Negatively
Discrimination, competition with advantaged peers Not at all
![Page 15: Randomized Evaluations: Applications Kenny Ajayi September 22, 2008 Global Poverty and Impact Evaluation.](https://reader035.fdocuments.net/reader035/viewer/2022062523/5a4d1ae77f8b9ab059979848/html5/thumbnails/15.jpg)
Do neighborhoods affect their residents?
Positively Social connections, role models, security, community
resources Negatively
Discrimination, competition with advantaged peers Not at all
Only family influences, genetic factors, individual human capital investments or broader non-neighborhood environment matter
![Page 16: Randomized Evaluations: Applications Kenny Ajayi September 22, 2008 Global Poverty and Impact Evaluation.](https://reader035.fdocuments.net/reader035/viewer/2022062523/5a4d1ae77f8b9ab059979848/html5/thumbnails/16.jpg)
Do neighborhoods affect their residents?
What is Y?
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Do neighborhoods affect their residents?
What is Y? Adults
Self sufficiency Physical & mental health
Youth Education (reading/math test scores) Physical & mental health Risky behavior
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Kling, Liebman and Katz (2007)
Do neighborhoods affect their residents? X → Y causality Y → X reverse causality X → Y and Y → X simultaneity Z → X and Z → Y omitted variables /
confounding X ‽ Y spurious correlation
![Page 19: Randomized Evaluations: Applications Kenny Ajayi September 22, 2008 Global Poverty and Impact Evaluation.](https://reader035.fdocuments.net/reader035/viewer/2022062523/5a4d1ae77f8b9ab059979848/html5/thumbnails/19.jpg)
Neighborhood Effects
Natural Experiment
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Neighborhood Effects
Natural Experiment Hurricane Katrina
Can we randomly assign people to live in different neighborhoods?
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Neighborhood Effects
Natural Experiment Hurricane Katrina
Can we randomly assign people to live in different neighborhoods? Sort of…
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Moving to Opportunity
Solicit volunteers to participate Randomly assign each volunteer an
option Control Section 8 Section 8 with restriction (low poverty neighborhood)
Volunteers decide whether to move Compliers: use voucher Non-compliers: don’t move
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Moving to Opportunity
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Non-random Participation
Why is this OK? Volunteers presumably care about their
neighborhood environment, so should be the target when thinking about uptake for the use of housing vouchers
BUT Results might not generalize to other populations
with different characteristics
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Treatment and Compliance
With perfect compliance: Pr(X=1│Z=1)=1 Pr(X=1│Z=0)=0
With imperfect compliance: 1>Pr(X=1│Z=1)>Pr(X=1│Z=0)>0
Where X - actual treatmentZ - assigned treatment
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Some Econometrics
Intention-to-treat (ITT) Effects Differences between treatment and control group
means
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Some Econometrics
Intention-to-treat (ITT) Effects Differences between treatment and control group means
ITT = E(Y|Z = 1) – E(Y|Z = 0)
Y - outcome Z - assigned treatment
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Some Econometrics
Intention-to-treat (ITT) Effects Writing this relationship in mathematical notation
Y = Zπ1 + Wβ1 + ε1
Individual wellbeing (Y) depends on whether or not the individual is assigned to treatment (Z), baseline characteristics
(W), and whether or not the individual got lucky (ε)
π1 - effect of being assigned to treatment group = (effect of actually moving) x (compliance rate)
![Page 29: Randomized Evaluations: Applications Kenny Ajayi September 22, 2008 Global Poverty and Impact Evaluation.](https://reader035.fdocuments.net/reader035/viewer/2022062523/5a4d1ae77f8b9ab059979848/html5/thumbnails/29.jpg)
Some Econometrics
Effect of Treatment on the Treated (TOT)
Differences between treatment and control group means
Differences in compliance for treatment and control groups
![Page 30: Randomized Evaluations: Applications Kenny Ajayi September 22, 2008 Global Poverty and Impact Evaluation.](https://reader035.fdocuments.net/reader035/viewer/2022062523/5a4d1ae77f8b9ab059979848/html5/thumbnails/30.jpg)
Some Econometrics
Effect of Treatment on the Treated (TOT) Differences between treatment and control group meansDifferences in compliance for treatment and control groups
TOT = “Wald Estimator” = E(Y|Z = 1) – E(Y|Z = 0)E(X|Z = 1) – E(X|Z = 0)
Y - outcome Z - assigned treatment
X - actual treatment (i.e. compliance)
![Page 31: Randomized Evaluations: Applications Kenny Ajayi September 22, 2008 Global Poverty and Impact Evaluation.](https://reader035.fdocuments.net/reader035/viewer/2022062523/5a4d1ae77f8b9ab059979848/html5/thumbnails/31.jpg)
Some More Econometrics
Estimate TOT using instrumental variables Use offer of a MTO voucher as an instrument for
MTO voucher use
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Some More Econometrics
Estimate TOT using instrumental variables Use offer of a MTO voucher as an instrument for
MTO voucher use. In mathematical notation:(1) X = Zρd + Wβd + εd
1. Predict whether people use voucher (X), given their assigned treatment (Z) and observable characteristics (W)
![Page 33: Randomized Evaluations: Applications Kenny Ajayi September 22, 2008 Global Poverty and Impact Evaluation.](https://reader035.fdocuments.net/reader035/viewer/2022062523/5a4d1ae77f8b9ab059979848/html5/thumbnails/33.jpg)
Some More Econometrics
Estimate TOT using instrumental variables Use offer of a MTO voucher as an instrument for MTO
voucher use. In mathematical notation:(1) X = Zρd + Wβd + εd
(2) Y = Xγ2 + Wβ2 + ε2
1. Predict whether people use voucher (X), given their assigned treatment (Z) and observable characteristics (W)
2. Estimate how much individual wellbeing (Y) depends on predicted compliance (X) and observable characteristics (W)
![Page 34: Randomized Evaluations: Applications Kenny Ajayi September 22, 2008 Global Poverty and Impact Evaluation.](https://reader035.fdocuments.net/reader035/viewer/2022062523/5a4d1ae77f8b9ab059979848/html5/thumbnails/34.jpg)
Some Econometrics
Effect of Treatment on the Treated (TOT)
Writing the reduced-form relationship in mathematical notation Y = Xγ2 + Wβ2 + ε2
Individual wellbeing (Y) depends on whether or not the individual complies with treatment (X), baseline
characteristics (W), and whether or not the individual got lucky (ε)
Note: γ2- effect of actually moving (i.e. compliance) = effect of being assigned to treatment
group compliance rate
![Page 35: Randomized Evaluations: Applications Kenny Ajayi September 22, 2008 Global Poverty and Impact Evaluation.](https://reader035.fdocuments.net/reader035/viewer/2022062523/5a4d1ae77f8b9ab059979848/html5/thumbnails/35.jpg)
Moving to Opportunity
![Page 36: Randomized Evaluations: Applications Kenny Ajayi September 22, 2008 Global Poverty and Impact Evaluation.](https://reader035.fdocuments.net/reader035/viewer/2022062523/5a4d1ae77f8b9ab059979848/html5/thumbnails/36.jpg)
Moving to Opportunity
INTENTION TO TREAT
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Moving to Opportunity
EFFECT OF TREATMENT ON THE TREATED
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Results Positive effects
Teenage girls Adult mental health
Negative effects Teenage boys
No effects Adult economic self-sufficiency & physical health Younger children
Increasing effects for lower poverty rates
![Page 39: Randomized Evaluations: Applications Kenny Ajayi September 22, 2008 Global Poverty and Impact Evaluation.](https://reader035.fdocuments.net/reader035/viewer/2022062523/5a4d1ae77f8b9ab059979848/html5/thumbnails/39.jpg)
Limitations
Can’t fully separate relocation effect from neighborhood effect
Can’t observe spillovers into receiving neighborhoods
![Page 40: Randomized Evaluations: Applications Kenny Ajayi September 22, 2008 Global Poverty and Impact Evaluation.](https://reader035.fdocuments.net/reader035/viewer/2022062523/5a4d1ae77f8b9ab059979848/html5/thumbnails/40.jpg)
Some Thoughts
We can still estimate impacts in cases where we don’t have full compliance
Policies sometimes target people who are different from the general population (i.e. those more likely to take up a program)
BUT We have to be careful about generalizing
these results for other populations (external validity)
![Page 41: Randomized Evaluations: Applications Kenny Ajayi September 22, 2008 Global Poverty and Impact Evaluation.](https://reader035.fdocuments.net/reader035/viewer/2022062523/5a4d1ae77f8b9ab059979848/html5/thumbnails/41.jpg)
Practical Applications
Noncompliance
Cost Benefit Analysis
Theory and Mechanisms
![Page 42: Randomized Evaluations: Applications Kenny Ajayi September 22, 2008 Global Poverty and Impact Evaluation.](https://reader035.fdocuments.net/reader035/viewer/2022062523/5a4d1ae77f8b9ab059979848/html5/thumbnails/42.jpg)
Duflo, Kremer & Robinson (2008)
Why don’t Kenyan farmers use fertilizer?
![Page 43: Randomized Evaluations: Applications Kenny Ajayi September 22, 2008 Global Poverty and Impact Evaluation.](https://reader035.fdocuments.net/reader035/viewer/2022062523/5a4d1ae77f8b9ab059979848/html5/thumbnails/43.jpg)
Duflo, Kremer & Robinson (2008)
Why don’t Kenyan farmers use fertilizer? Low returns High cost Preference for traditional farming methods Lack of information
![Page 44: Randomized Evaluations: Applications Kenny Ajayi September 22, 2008 Global Poverty and Impact Evaluation.](https://reader035.fdocuments.net/reader035/viewer/2022062523/5a4d1ae77f8b9ab059979848/html5/thumbnails/44.jpg)
Is Fertilizer Use Profitable?
What is Y (outcome)? Crop yield Rate of return over the season
What is X (observed)? Fertilizer Hybrid seed
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Is Fertilizer Use Profitable?
What is Z (unobserved)?
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Is Fertilizer Use Profitable?
What is Z (unobserved)? Farmer experience/ability Farmer resources Weather Soil quality Market conditions
![Page 47: Randomized Evaluations: Applications Kenny Ajayi September 22, 2008 Global Poverty and Impact Evaluation.](https://reader035.fdocuments.net/reader035/viewer/2022062523/5a4d1ae77f8b9ab059979848/html5/thumbnails/47.jpg)
Correlation or Causation?
Fertilizer Use (X) and Crop Yield (Y)
![Page 48: Randomized Evaluations: Applications Kenny Ajayi September 22, 2008 Global Poverty and Impact Evaluation.](https://reader035.fdocuments.net/reader035/viewer/2022062523/5a4d1ae77f8b9ab059979848/html5/thumbnails/48.jpg)
Correlation or Causation?
Fertilizer Use (X) and Crop Yield (Y)
X → Y causality Y → X reverse causality X → Y and Y → X simultaneity Z → X and Z → Y omitted variables /
confounding X ‽ Y spurious correlation
![Page 49: Randomized Evaluations: Applications Kenny Ajayi September 22, 2008 Global Poverty and Impact Evaluation.](https://reader035.fdocuments.net/reader035/viewer/2022062523/5a4d1ae77f8b9ab059979848/html5/thumbnails/49.jpg)
Fertilizer Experiment
How could we test the effect of fertilizer use on crop yield using randomization?
![Page 50: Randomized Evaluations: Applications Kenny Ajayi September 22, 2008 Global Poverty and Impact Evaluation.](https://reader035.fdocuments.net/reader035/viewer/2022062523/5a4d1ae77f8b9ab059979848/html5/thumbnails/50.jpg)
Fertilizer Experiment
Randomly select participating farmers Measure 3 adjacent equal-sized plots on
each farm Randomly assign farming package for
each plot Top dressing fertilizer (T1) Fertilizer and hybrid seed (T2) Traditional seed (C)
Weigh each plot’s yield at harvest
![Page 51: Randomized Evaluations: Applications Kenny Ajayi September 22, 2008 Global Poverty and Impact Evaluation.](https://reader035.fdocuments.net/reader035/viewer/2022062523/5a4d1ae77f8b9ab059979848/html5/thumbnails/51.jpg)
Fertilizer Experiment
Calculate rate of return over the season:
value of value oftreatment - comparison - value ofplot output plot output input
RR = ----------------------------------------------------------- - 100
value of input
![Page 52: Randomized Evaluations: Applications Kenny Ajayi September 22, 2008 Global Poverty and Impact Evaluation.](https://reader035.fdocuments.net/reader035/viewer/2022062523/5a4d1ae77f8b9ab059979848/html5/thumbnails/52.jpg)
Results
![Page 53: Randomized Evaluations: Applications Kenny Ajayi September 22, 2008 Global Poverty and Impact Evaluation.](https://reader035.fdocuments.net/reader035/viewer/2022062523/5a4d1ae77f8b9ab059979848/html5/thumbnails/53.jpg)
Results
![Page 54: Randomized Evaluations: Applications Kenny Ajayi September 22, 2008 Global Poverty and Impact Evaluation.](https://reader035.fdocuments.net/reader035/viewer/2022062523/5a4d1ae77f8b9ab059979848/html5/thumbnails/54.jpg)
Results
![Page 55: Randomized Evaluations: Applications Kenny Ajayi September 22, 2008 Global Poverty and Impact Evaluation.](https://reader035.fdocuments.net/reader035/viewer/2022062523/5a4d1ae77f8b9ab059979848/html5/thumbnails/55.jpg)
Some Thoughts
More comprehensive package increased yieldBUT Lower cost package had highest rate of return
Lab results don’t always apply in the field
Think about the COSTS and BENEFITS
![Page 56: Randomized Evaluations: Applications Kenny Ajayi September 22, 2008 Global Poverty and Impact Evaluation.](https://reader035.fdocuments.net/reader035/viewer/2022062523/5a4d1ae77f8b9ab059979848/html5/thumbnails/56.jpg)
Practical Applications
Noncompliance
Cost Benefit Analysis
Theory and Mechanisms
![Page 57: Randomized Evaluations: Applications Kenny Ajayi September 22, 2008 Global Poverty and Impact Evaluation.](https://reader035.fdocuments.net/reader035/viewer/2022062523/5a4d1ae77f8b9ab059979848/html5/thumbnails/57.jpg)
Karlan and Zinman (2007)
What is the optimal loan contract?
![Page 58: Randomized Evaluations: Applications Kenny Ajayi September 22, 2008 Global Poverty and Impact Evaluation.](https://reader035.fdocuments.net/reader035/viewer/2022062523/5a4d1ae77f8b9ab059979848/html5/thumbnails/58.jpg)
Objectives
Profitability Gross Revenue Repayment
Targeted Access Poor households Female microentrepreneurs
Sustainability
![Page 59: Randomized Evaluations: Applications Kenny Ajayi September 22, 2008 Global Poverty and Impact Evaluation.](https://reader035.fdocuments.net/reader035/viewer/2022062523/5a4d1ae77f8b9ab059979848/html5/thumbnails/59.jpg)
Theory of Change
Loan Profitscontract
Mechanisms How do we expect a change to affect the outcome? What assumptions are we making? Can we test if they are true?
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Loan Offer Experiment
What is Y? Take-up Loan size Default
What is X? Interest rate Maturity
How sensitive are borrowers to interest rates and maturities?
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Loan Offer Experiment
![Page 62: Randomized Evaluations: Applications Kenny Ajayi September 22, 2008 Global Poverty and Impact Evaluation.](https://reader035.fdocuments.net/reader035/viewer/2022062523/5a4d1ae77f8b9ab059979848/html5/thumbnails/62.jpg)
Results
Downward sloping demand curves
Loan size more responsive to loan maturity than to interest rate
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Some Limitations
Again, results apply only to people who had previously borrowed from the lender.
Elasticities apply to direct mail solicitations and might not hold for other loan offer methods
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Final Thoughts
Randomized evaluation can have many applications beyond merely looking at reduced-form relationships between
X and Y