Impact Evaluation
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Transcript of Impact Evaluation
What is Impact Evaluation?
� IE assesses how a program affects the well-being or
welfare of individuals, households or communities (or
businesses)
� Well-being at the individual level can be captured by
income & consumption, health outcomes or ideally
both
� At the community level, poverty levels or growth rates
may be appropriate, depending on the question
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Outline
� Advantages of Impact Evaluation
� Challenges for IE: Need for Comparison Groups
� Methods for Constructing Comparison
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IE Versus other M&E Tools
� The key distinction between impact evaluation
and other M&E tools is the focus on discerning the
impact of the program from all other confounding
effects
� IE seeks to provide evidence of the causal link
between an intervention and outcomes
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Monitoring and IE
IMPACT
OUTPUTS
OUTCOMES
INPUTS
Effect on living standards and welfare
- infant and child mortality, - improved household income
Financial and physical resources - spending in primary health care
Goods and services generated
- number of nurses- availability of medicine
Access, usage and satisfaction of users
- number of children vaccinated, - percentage within 5 km of health center
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Monitoring and IE
Gov’t/program
production
function
Users meet
service
delivery
INPUTS
OUTPUTS
OUTCOMES
IMPACTSProgram impacts
confounded by local,
national, global effects
difficulty
of
showing
causality
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Logic Model: An Example
� Consider a program of providing Insecticide-Treated Nets (ITNs) to
poor households
� What are:
� Inputs?
� Outputs?
� Outcomes?
� Impacts?
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Logic Model: An Example
� Inputs: # of ITNs; # of health or NGO employees to help
dissemination
� Outputs: # of ITNs received by HHs
� Outcomes: ITNs utilized by # of households
� Impact: Reduction in illness from malaria; increase in income;
improvements in children’s school attendance and performance
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Advantages of IE
� In order to be able to determine which projects are successful, need a carefully designed impact evaluation strategy
� This is useful for:� Understanding if projects worked:
� Justification for funding
� Scaling up
� Meta-analysis: Learning from Others
� Cost-benefit tradeoffs across projects
� Can test between different approaches of same program or different projects to meet national indicator
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Essential Methodology
� Difficulty is determining what would have happened to the individuals or communities of interest in absence of the project
� The key component to an impact evaluation is to construct a suitable comparison group to proxy for the “counterfactual”
� Problem: can only observe people in one state of the world at one time
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Before/After Comparisons
� Why not collect data on individuals before and after intervention (the Reflexive)? Difference in income, etc, would be due to project
� Problem: many things change over time, including the project
� The country is growing and ITN usage is increasing generally (from 2000-2003 in NetMark data), so how do we know an increase in ITN use is due to the program or would have occurred in absence of program?
� Many factors affect malaria rate in a given year
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Example: Providing Insecticide-
Treated Nets (ITNs) to Poor
Households� The intervention: provide free ITNs to households
in Zamfara
� Program targets poor areas
� Women have to enroll at local NGO office in order to receive bednets
� Starts in 2002, ends in 2003, we have data on malaria rates from 2001-
2004
� Scenario 1: we observe that the households in
Zamfara we provided bednets to have an increase
malaria from 2002 to 2003
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Years
Malaria Rate
2001 2002 2003 2004Treatment Period
A
CImpact = C – A?An increase in malaria rate!
Underestimated Impact when
using before/after comparisons: High rainfall year
Basic Problem of Impact Evaluation:
Scenario 1
Zamfara households with bednets
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“Counterfactual”Zamfara Households if no bednets provided
Years
Malaria Rate
2001 2002 2003 2004Treatment Period
Impact = C – B
A Decline in the
Malaria Rate!
A
B
C
Impact ≠ C - A
Underestimated Impact when
using before/after comparisons: High rainfall year
Basic Problem of Impact Evaluation:
Scenario 1
Zamfara households with bednets
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“Counterfactual” (Zamfara households if no bednets provided)
Years
Malaria Rate
2001 2002 2003 2004Treatment Period
TRUE Impact = C - B
A
B
C
Overestimated Impact: Bad Rainfall
Impact ≠ C - A
Basic Problem of Impact Evaluation:
Scenario 2
Zamfara households
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Comparison Groups
� Instead of using before/after comparisons, we need to use comparison groups to proxy for the counterfactual
� Two Core Problems in Finding Suitable Groups:
� Programs are targeted
�Recipients receive intervention for particular reason
� Participation is voluntary
� Individuals who participate differ in observable and unobservable ways (selection bias)
• Hence, a comparison of participants and an arbitrary group of non-participants can lead to misleading or incorrect results
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Comparison 1: Treatment and
Region B� Scenario 1: Failure of reflexive comparison due to higher
rainfall, and everyone experienced an increase in malaria rates
� We compare the households in the program region to those in another region
� We find that our “treatment” households in Zamfara have a larger increase in malaria rates than those in region B, Oyo. Did the program have a negative impact?
� Not necessarily! Program placement is important:
�Region B has better sanitation and therefore affected less by rainfall (unobservable)
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Years
Malaria rate
2001 2002 2003 2004Treatment Period
High Rainfall
Basic Problem of Impact Evaluation:
Program Placement
“Treatment”: ZamfaraA
D
E
TRUE IMPACT: E-D
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Years
Malaria rate
2001 2002 2003 2004Treatment Period
Underestimated Impact when using region B
comparison group: High Rainfall
Basic Problem of Impact Evaluation:
Program Placement
“Treatment”: Zamfara
Region B: Oyo
A
B
C
D
E-A > C-B : Region B affected less by rainfall
E
TRUE IMPACT: E-D
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Comparison 2: Treatment vs.
Neighbors� We compare “treatment” households with their neighbors. We
think the sanitation and rainfall patterns are about the same.
� Scenario 2: Let’s say we observe that treatment households’ malaria rates decrease more than comparison households. Did the program work?
� Not necessarily: There may be two types of households: types A and B, with A knowing how malaria is transmitted and also burn mosquito coils
� Type A households were more likely to register with the program. However, their other characteristics mean they would have had lower malaria rates in the absence of the ITNs (individual unobservables).
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Type A HHs with Project
Years
Malaria Rates
Y1 Y2 Y3 Y4Treatment Period
Basic Problem of Impact Evaluation:
Selection Bias
Type B HHs
Observed difference
Comparing Project Beneficiaries (Type A) to
Neighbors (Type B)
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Type A HHs with Project
Type A Households
Years
Malaria Rates
Y1 Y2 Y3 Y4Treatment Period
Basic Problem of Impact Evaluation:
Selection Bias
Type B HHs
True Impact
Selection BiasObserved difference
Participants are often different than Non-participants
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Basic Problem of Impact Evaluation:
Spillover Effects
� Another difficulty finding a true counterfactual has to do will spillover or contagion effects
� Example: ITNs will not only reduce malaria rates for those sleeping under nets, but also may lower overall rates because ITNs kill mosquitoes
� Problem: children who did not receive “treatment” may also have lower malaria rates – and therefore higher school attendance rates
� Generally leads to underestimate of treatment effect
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“Treatment” Children
Years
School Attendance
2001 2002 2003 2004Treatment Period
Impact ≠ B - C
A
B
C
Impact = B - A
Basic Problem of Impact Evaluation:
Spillover Effects
“Control” Group of Children in Neighborhood School
C>A due to spilloverfrom treatment children
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Counterfactual: Methodology
� We need a comparison group that is as identical in observable and
unobservable dimensions as possible, to those receiving the
program, and a comparison group that will not receive spillover
benefits.
� Number of techniques:
�Randomization as gold standard
�Various Techniques of Matching
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How to construct a comparison
group – building the
counterfactual1. Randomization
2. Difference-in-Difference
3. Regression discontinuity
4. Matching
� Pipeline comparisons
� Propensity score
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1. Randomization
� Individuals/communities/firms are randomly assigned into participation
� Counterfactual: randomized-out group
� Advantages:
� Often addressed to as the “gold standard”: by design: selection bias is zero on average and mean impact is revealed
� Perceived as a fair process of allocation with limited resources
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Randomization: Disadvantages
� Disadvantages:
� Ethical issues, political constraints
� Internal validity (exogeneity): people might not comply with the
assignment (selective non-compliance)
� External validity (generalizability): usually run controlled experiment
on a pilot, small scale. Difficult to extrapolate the results to a larger
population.
� Does not always solve problem of spillovers
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When to Randomize
� If funds are insufficient to treat all eligible recipients
� Randomization can be the most fair and transparent approach
� The program is administered at the individual, household or community level
� Higher level of implementation difficult: example – trunk roads
� Program will be scaled-up: learning what works is very valuable
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2. Difference-in-difference
� Observations over time: compare observed changes in the outcomes for a sample of participants and non-participants
� Identification assumption: the selection bias or unobservable characteristics are time-invariant (‘parallel trends’ in the absence of the program)
� Counter-factual: changes over time for the non-participants
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Diff-in-Diff: Continued
Constraint: Requires at least two cross-sections of data,
pre-program and post-program on participants
and non-participants
� Need to think about the evaluation ex-ante, before the program
� More valid if there are 2 pre-periods so can observe whether trend is
same
� Can be in principle combined with matching to
adjust for pre-treatment differences that affect the
growth rate
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Implementing differences in
differences: Different Strategies
� Some arbitrary comparison group
� Matched diff in diff
� Randomized diff in diff
� These are in order of more problems � less problems, think about
this as we look at this graphically
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Essential Assumptions of Diff-in-Diff
� Initial
difference must
be time
invariant
� In absence
of program, the
change over
time would be
identical
Y1
Impact
Y1
*
Y0
t=0 t=1 time
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Difference-in-Difference in ITN
Example
� Instead of comparing Zamfara to Oyo, compare Zamfara to Niger if:
� While Zamfara and Oyo have different malaria rates and different ITN
usage, we expect that they change in parallel
� Use NetMark data to compare 2000 to 2003 in Zamfara and Niger
states
� Use additional data (GHS, NLSS) to compare incomes and sanitation
infrastructure levels and changes prior to program implementation
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3. Regression discontinuity design
� Exploit the rule generating assignment into a program given to individuals only above a given threshold – Assume that discontinuity in participation but not in counterfactual outcomes
� Counterfactual: individuals just below the cut-off who did not participate
� Advantages:
� “Identification” built in the program design
� Delivers marginal gains from the program around the eligibility cut-off point. Important for program expansion
� Disadvantages:
� Threshold has to be applied in practice, and individuals should not be able manipulate the score used in the program to become eligibleWali Memon35
RDD in ITN Example
� Program available for poor households
� Eligibility criteria: must be below the national poverty line or < 1 ha
of land
� Treatment group: those below cut-off
� Those with income below the poverty line and therefore qualified for
ITNs
� Comparison group: those right above the cutoff
� Those with income just above poverty line and therefore not-eligible
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RDD in ITN Example
� Problems:
� How well enforced was the rule?
� Can the rule be manipulated?
� Local effect: may not be generalizable if program expands to
households well above poverty line
� Particularly relevant since NetMark data indicate low ITN usage across all
socio-economic status groups
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4. Matching
� Match participants with non-participants from a larger survey
� Counterfactual: matched comparison group
� Each program participant is paired with one or more non-participant that are similar based on observable characteristics
� Assumes that, conditional on the set of observables, there is no selection bias based on unobserved heterogeneity
� When the set of variables to match is large, often match on a summary statistics: the probability of participation as a function of the observables (the propensity score)
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4. Matching
� Advantages:
� Does not require randomization, nor baseline (pre-intervention data)
� Disadvantages:
� Strong identification assumptions
� In many cases, may make interpretation of results very difficult
� Requires very good quality data: need to control for all factors that influence program placement
� Requires significantly large sample size to generate comparison group
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Matching in Practice
� Using statistical techniques, we match a group of non-
participants with participants using variables like gender,
household size, education, experience, land size (rainfall to
control for drought), irrigation (as many observable
characteristics not affected by program intervention)
� One common method: Propensity Score Matching
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Matching in Practice: 2
Approaches� Approach 1: After program implementation, we match
(within region) those who received ITNs with those who did not. Problem?
�Problem: likelihood of usage of different households is unobservable, so not included in propensity score
�This creates selection bias
� Approach 2: The program is allocated based on land size. After implementation, we match those eligible in region A with those in region B. Problem?
�Problems: same issues of individual unobservables, but lessened because we compare eligible to potential eligible
�Now problem of unobservable factors across regions
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An extension of matching:
pipeline comparisons
� Idea: compare those just about to get an intervention with those getting it now
� Assumption: the stopping point of the intervention does not separate two fundamentally different populations
� Example: extending irrigation networks
� In ITN example: If only some communities within Zamfarareceive ITNs in round 1: compare them to nearby communities will receive ITNs in round 2
� Difficulty with Infrastructure: Spillover effects may be strong or anticipatory effect
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