randomized evaluations applications
Download
Skip this Video
Download Presentation
Randomized Evaluations: Applications

Loading in 2 Seconds...

play fullscreen
1 / 64

Randomized Evaluations: Applications - PowerPoint PPT Presentation


  • 61 Views
  • Uploaded on

Randomized Evaluations: Applications. Kenny Ajayi September 22, 2008. Global Poverty and Impact Evaluation. Review. Causation and Correlation (5 factors) X → Y Y → X X → Y and Y → X Z → X and Z → Y X ‽ Y . Review. Causation and Correlation (5 factors) X → Y causality

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

PowerPoint Slideshow about ' Randomized Evaluations: Applications' - shiro


An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
randomized evaluations applications

Randomized Evaluations:Applications

Kenny Ajayi

September 22, 2008

Global Poverty and Impact Evaluation

review
Review
  • Causation and Correlation (5 factors)
    • X → Y
    • Y → X
    • X → Y and Y → X
    • Z → X and Z → Y
    • X ‽ Y
review1
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
review2
Review

Exogenous

review3
Review

Exogenous

Endogenous

review4
Review

Exogenous

Endogenous

External Validity

review5
Review

Exogenous

Endogenous

External Validity

Natural Experiment

practical applications
Practical Applications
  • Noncompliance
  • Cost Benefit Analysis
  • Theory and Mechanisms
kling liebman and katz 2007
Kling, Liebman and Katz (2007)
  • Do neighborhoods affect their residents?
do neighborhoods affect their residents1
Do neighborhoods affect their residents?
  • Positively
      • Social connections, role models, security, community resources
do neighborhoods affect their residents2
Do neighborhoods affect their residents?
  • Positively
      • Social connections, role models, security, community resources
  • Negatively
do neighborhoods affect their residents3
Do neighborhoods affect their residents?
  • Positively
      • Social connections, role models, security, community resources
  • Negatively
      • Discrimination, competition with advantaged peers
do neighborhoods affect their residents4
Do neighborhoods affect their residents?
  • Positively
      • Social connections, role models, security, community resources
  • Negatively
      • Discrimination, competition with advantaged peers
  • Not at all
do neighborhoods affect their residents5
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
do neighborhoods affect their residents7
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
kling liebman and katz 20071
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
neighborhood effects
Neighborhood Effects
  • Natural Experiment
neighborhood effects1
Neighborhood Effects
  • Natural Experiment
    • Hurricane Katrina
  • Can we randomly assign people to live in different neighborhoods?
neighborhood effects2
Neighborhood Effects
  • Natural Experiment
    • Hurricane Katrina
  • Can we randomly assign people to live in different neighborhoods?
    • Sort of…
moving to opportunity
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
non random participation
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
treatment and compliance
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 treatment

Z - assigned treatment

some econometrics
Some Econometrics
  • Intention-to-treat (ITT) Effects
      • Differences between treatment and control group means
some econometrics1
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

some econometrics2
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)

some econometrics3
Some Econometrics
  • Effect of Treatment on the Treated (TOT)
      • Differences between treatment and control group means

Differences in compliance for treatment and control groups

some econometrics4
Some Econometrics
  • Effect of Treatment on the Treated (TOT)
      • Differences between treatment and control group means

Differences 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)

some more econometrics
Some More Econometrics
  • Estimate TOT using instrumental variables
    • Use offer of a MTO voucher as an instrument for MTO voucher use
some more econometrics1
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

    • Predict whether people use voucher (X), given their assigned treatment (Z) and observable characteristics (W)
some more econometrics2
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

    • Predict whether people use voucher (X), given their assigned treatment (Z) and observable characteristics (W)
    • Estimate how much individual wellbeing (Y) depends on predicted compliance (X) and observable characteristics (W)
some econometrics5
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 actuallymoving (i.e. compliance)

= effect of being assigned to treatment group compliance rate

moving to opportunity3
Moving to Opportunity

INTENTION TO TREAT

moving to opportunity4
Moving to Opportunity

EFFECT OF TREATMENT ON THE TREATED

results
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
limitations
Limitations
  • Can’t fully separate relocation effect from neighborhood effect
  • Can’t observe spillovers into receiving neighborhoods
some thoughts
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)
practical applications1
Practical Applications

Noncompliance

Cost Benefit Analysis

Theory and Mechanisms

duflo kremer robinson 2008
Duflo, Kremer & Robinson (2008)
  • Why don’t Kenyan farmers use fertilizer?
duflo kremer robinson 20081
Duflo, Kremer & Robinson (2008)
  • Why don’t Kenyan farmers use fertilizer?
    • Low returns
    • High cost
    • Preference for traditional farming methods
    • Lack of information
is fertilizer use profitable
Is Fertilizer Use Profitable?
  • What is Y (outcome)?
    • Crop yield
    • Rate of return over the season
  • What is X (observed)?
    • Fertilizer
    • Hybrid seed
is fertilizer use profitable1
Is Fertilizer Use Profitable?
  • What is Z (unobserved)?
is fertilizer use profitable2
Is Fertilizer Use Profitable?
  • What is Z (unobserved)?
    • Farmer experience/ability
    • Farmer resources
    • Weather
    • Soil quality
    • Market conditions
correlation or causation
Correlation or Causation?

Fertilizer Use (X) and Crop Yield (Y)

correlation or causation1
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
fertilizer experiment
Fertilizer Experiment
  • How could we test the effect of fertilizer use on crop yield using randomization?
fertilizer experiment1
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
fertilizer experiment2
Fertilizer Experiment
  • Calculate rate of return over the season:

value of value of

treatment - comparison - value of

plot output plot output input

RR = ----------------------------------------------------------- - 100

value of input

some thoughts1
Some Thoughts
  • More comprehensive package increased yield

BUT

  • Lower cost package had highest rate of return
  • Lab results don’t always apply in the field
  • Think about the COSTS and BENEFITS
practical applications2
Practical Applications

Noncompliance

Cost Benefit Analysis

Theory and Mechanisms

karlan and zinman 2007
Karlan and Zinman (2007)
  • What is the optimal loan contract?
objectives
Objectives
  • Profitability
    • Gross Revenue
    • Repayment
  • Targeted Access
    • Poor households
    • Female microentrepreneurs
  • Sustainability
theory of change
Theory of Change

Loan Profits

contract

  • Mechanisms
    • How do we expect a change to affect the outcome?
    • What assumptions are we making?
    • Can we test if they are true?
loan offer experiment
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?
results4
Results
  • Downward sloping demand curves
  • Loan size more responsive to loan maturity than to interest rate
some limitations
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
final thoughts
Final Thoughts
  • Randomized evaluation can have many applications beyond merely looking at reduced-form relationships between

X and Y

ad