Impact Evaluation:

An Overview Lori Beaman, PhD RWJF Scholar in Health Policy UC Berkeley

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

Outline

 Advantages of Impact Evaluation  Challenges for IE: Need for Comparison Groups  Methods for Constructing Comparison

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

Monitoring and IE

IMPACT OUTCOMES OUTPUTS INPUTS Effect on living standards and welfare

- infant and child mortality, - improved household income

Access, usage and satisfaction of users

- number of children vaccinated, - percentage within 5 km of health center

Goods and services generated

- number of nurses - availability of medicine

Financial and physical resources

- spending in primary health care

Monitoring and IE

IMPACTS Program impacts confounded by local, national, global effects Users meet service delivery Gov’t/program production function OUTCOMES OUTPUTS INPUTS difficulty of showing causality

Logic Model: An Example

  Consider a program of providing Insecticide-Treated Nets (ITNs) to poor households What are:     Inputs?

Outputs?

Outcomes?

Impacts?

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

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

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

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

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

Basic Problem of Impact Evaluation: Scenario 1

Malaria Rate

Underestimated Impact when using before/after comparisons: High rainfall year

Zamfara households with bednets Impact = C – A?

An increase in malaria rate!

A C 2001 2002 Treatment Period 2003 2004 Years

Basic Problem of Impact Evaluation: Scenario 1

Malaria Rate

Underestimated Impact when using before/after comparisons: High rainfall year

“Counterfactual” Zamfara Households if no bednets provided B Zamfara households with bednets C

Impact = C – B A Decline in the Malaria Rate!

Impact ≠ C - A

A 2001 2002 Treatment Period 2003 2004 Years

Basic Problem of Impact Evaluation: Scenario 2

Malaria Rate

Overestimated Impact: Bad Rainfall

2001

Impact ≠ C - A

A B C TRUE Impact = C - B “Counterfactual” (Zamfara households if no bednets provided) Zamfara households 2002 Treatment Period 2003 2004 Years

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

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)

Basic Problem of Impact Evaluation: Program Placement

Malaria rate

High Rainfall

A 2001 2002 Treatment Period 2003 D TRUE IMPACT: E-D E “Treatment”: Zamfara 2004 Years

Basic Problem of Impact Evaluation: Program Placement

Malaria rate

Underestimated Impact when using region B comparison group: High Rainfall E-A > C-B : Region B affected less by rainfall

Region B: Oyo C B D TRUE IMPACT: E-D E “Treatment”: Zamfara A 2001 2002 Treatment Period 2003 2004 Years

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).

Basic Problem of Impact Evaluation: Selection Bias

Malaria Rates

Comparing Project Beneficiaries (Type A) to Neighbors (Type B)

Observed difference

Type B HHs Type A HHs with Project

Y1 Y2 Treatment Period Y3 Y4 Years

Basic Problem of Impact Evaluation: Selection Bias

Participants are often different than Non-participants

Malaria Rates Observed difference

Type B HHs

Selection Bias True Impact

Type A Households Type A HHs with Project

Y1 Y2 Treatment Period Y3 Y4 Years

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

Basic Problem of Impact Evaluation: Spillover Effects

School Attendance “Treatment” Children Impact = B - A A B

Impact ≠ B - C

C C>A due to spillover from treatment children “Control” Group of Children in Neighborhood School 2001 2002 Treatment Period 2003 2004 Years

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

1.

2.

3.

4.

How to construct a comparison group – building the counterfactual

Randomization Difference-in-Difference Regression discontinuity Matching   Pipeline comparisons Propensity score

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

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

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

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

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

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 at this graphically  less problems, think about this as we look

Essential Assumptions of Diff-in-Diff

 Initial difference must be time invariant

Y

1

Y

1 *

Impact

 In absence over time would be identical of program,

Y

0

t

=0

t

=1

time

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

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 eligible

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

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



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)

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

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

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

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 Zamfara receive 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