1. An introduction to Impact Evaluation
Markus Goldstein
Poverty Reduction Group
The World Bank

2. My question is: Are we making an impact?

3. 2 parts Impact evaluation methods
Impact evaluation practicalities: IE and the project cycle
Use rural project examples

4. Outline - methods Monitoring and impact evaluation
Why do impact evaluation
Why we need a comparison group
Methods for constructing the comparison group
When to do an impact evaluation

5. Monitoring and IE
Example here is an agricultural extension project

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

7. Impact evaluation
Many names (e.g. Rossi et al call this impact assessment) so need to know the concept.
Impact is the difference between outcomes with the program and without it
The goal of impact evaluation is to measure this difference in a way that can attribute the difference to the program, and only the program

8. Why it matters
We want to know if the program had an impact and the average size of that impact
Understand if policies work
Justification for program (big $$)
Scale up or not – did it work?
Meta-analyses – learning from others
(with cost data) understand the net benefits of the program
Understand the distribution of gains and losses

9. What we need
 The difference in outcomes with the program versus without the program – for the same unit of analysis (e.g. individual)
Problem: individuals only have one existence
Hence, we have a problem of a missing counter-factual, a problem of missing data

10. Thinking about the counterfactual
Why not compare individuals before and after (the reflexive)?
The rest of the world moves on and you are not sure what was caused by the program and what by the rest of the world
We need a control/comparison group that will allow us to attribute any change in the “treatment” group to the program (causality)

11. comparison group issues
Two central problems:
Programs are targeted
 Program areas will differ in observable and unobservable ways precisely because the program intended this
Individual participation is (usually) voluntary
Participants will differ from non-participants in observable and unobservable ways
Hence, a comparison of participants and an arbitrary group of non-participants can lead to heavily biased results

12. Example: providing fertilizer to farmers
The intervention: provide fertilizer to farmers in a poor region of a country (call it region A)
Program targets poor areas
Farmers have to enroll at the local extension office to receive the fertilizer
Starts in 2002, ends in 2004, we have data on yields for farmers in the poor region and another region (region B) for both years
We observe that the farmers we provide fertilizer to have a decrease in yields from 2002 to 2004

13. Did the program not work?
Further study reveals there was a national drought, and everyone’s yields went down (failure of the reflexive comparison)
We compare the farmers in the program region to those in another region. We find that our “treatment” farmers have a larger decline than those in region B. Did the program have a negative impact?
Not necessarily (program placement)
Farmers in region B have better quality soil (unobservable)
Farmers in the other region have more irrigation, which is key in this drought year (observable)

14. OK, so let’s compare the farmers in region A
We compare “treatment” farmers with their neighbors. We think the soil is roughly the same.
Let’s say we observe that treatment farmers’ yields decline by less than comparison farmers. Did the program work?
Not necessarily. Farmers who went to register with the program may have more ability, and thus could manage the drought better than their neighbors, but the fertilizer was irrelevant. (individual unobservables)
Let’s say we observe no difference between the two groups. Did the program not work?
Not necessarily. What little rain there was caused the fertilizer to run off onto the neighbors’ fields. (spillover/contamination)

15. The comparison group
In the end, with these naïve comparisons, we cannot tell if the program had an impact
 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.

16. How to construct a comparison group – building the counterfactual
Randomization
Matching
Difference-in-Difference
Instrumental variables
Regression discontinuity

17. 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
Disadvantages:
Ethical issues, political constraints
Internal validity (exogeneity): people might not comply with the assignment (selective non-compliance)
Unable to estimate entry effect
External validity (generalizability): usually run controlled experiment on a pilot, small scale. Difficult to extrapolate the results to a larger population.

18. Randomization in our example…
Simple answer: randomize farmers within a community to receive fertilizer...
Potential problems?
Run-off (contamination) so control for this
Take-up (what question are we answering)

19. 2. 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)

20. 2. Matching Advantages: Disadvantages:
Does not require randomization, nor baseline (pre-intervention data)
Disadvantages:
Strong identification assumptions
Requires very good quality data: need to control for all factors that influence program placement
Requires significantly large sample size to generate comparison group

21. Matching in our example…
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 charachteristics not affected by fertilizer)

22. Matching in our example… 2 scenarios
Scenario 1: We show up afterwards, we can only match (within region) those who got fertilizer with those who did not. Problem?
Problem: select on expected gains and/or ability (unobservable)
Scenario 2: The program is allocated based on historical crop choice and land size. We show up afterwards and 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 unobservables across regions

23. 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

24. 3. 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 is time-invariant (‘parallel trends’ in the absence of the program)
Counter-factual: changes over time for the non-participants
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
Can be in principle combined with matching to adjust for pre-treatment differences that affect the growth rate

25. Implementing differences in differences in our example…
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

26. As long as the bias is additive and time-invariant, diff-in-diff will work ….

27. What if the observed changes over time are affected?

28. 4. Instrumental Variables
Identify variables that affects participation in the program, but not outcomes conditional on participation (exclusion restriction)
Counterfactual: The causal effect is identified out of the exogenous variation of the instrument
Advantages:
Does not require the exogeneity assumption of matching
Disadvantages:
The estimated effect is local: IV identifies the effect of the program only for the sub-population of those induced to take-up the program by the instrument
Therefore different instruments identify different parameters. End up with different magnitudes of the estimated effects
Validity of the instrument can be questioned, cannot be tested.

29. IV in our example
It turns out that outreach was done randomly…so the time/intake of farmers into the program is essentially random.
We can use this as an instrument
Problems?
Is it really random? (roads, etc)

30. 5.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.

31. Example from Buddelmeyer and Skoufias, 2005

32. RDD in our example…
Back to the eligibility criteria: land size and crop history
We use those right below the cut-off and compare them with those right above…
Problems:
How well enforced was the rule?
Can the rule be manipulated?
Local effect

33. Discussion example: building a control group for irrigation
Scenario: we have a project to extend existing reaches and build some new canal
An initial analysis shows that farmers who are newly irrigated have increased yield…was the project a success?
What is the evaluation question?
What is a logical comparison group and method?

34. Investment operation vs adjustment/budget support
Project
Maybe evaluate all, but unlikely
Pick subcomponents
Adjustment/budget support
Build a strong M&E unit
Impact evaluation designed by govt
Evaluate policy reform pilots
e.g. health insurance pilot, P4P, tariff changes
Anything economy wide ≠ impact evaluation

35. Prioritizing for Impact Evaluation
It is not cheap – relative to monitoring
Possible prioritization criteria:
Don’t know if policy is effective
e.g. conditional cash transfers
Politics
e.g. Argentina workfare program
It’s a lot of money
Note that 2 & 3 are variants of not “knowing” – in this context, etc.

36. Summing up: Methods
No clear “gold standard” in reality – do what works best in the context
Watch for unobservables, but don’t forget observables
Be flexible, be creative – use the context
IE requires good monitoring and monitoring will help you understand the effect size

37. Impact Evaluation and the Project Cycle

38. Objective of this part of the presentation
Walk you through what it takes to do an impact evaluation for your project from Identification to ICR
Persuade you that impact evaluation will add value to your project

39. We will talk about… General Principles
In the context of 3 project periods:
Evaluation activities – the core issues for evaluation design and implementation, and
Housekeeping activities—procedural, administrative and financial management issues
Where to go for assistance

40. Some general principles
Government ownership as whole—what matters is institutional buy-in so that the results get used
Relevance and applicability—asking the right questions
Flexibility and adaptability
Horizon matters

41. Ownership
IE can provide one avenue to build institutional capacity and a culture of managing-by-results – so the IE should be as widely owned within gov’t as possible
Agree on a dissemination plan to maximize use of results for policy development.
Identify entry points in project and policy cycles
midpoint and closing, for project;
sector reporting, CGs, MTEF, budget, for WB
Budget cycles, policy reviews for gov’t
Use partnerships with local academics to build local capacity for impact evaluation.

42. Relevance and Applicability
For an evaluation to be relevant, it must be designed to respond to the policy questions that are of importance.
Clarifying early what it is that will be learned and designing the evaluation to that end will go some way to ensure that the recommendations of the evaluation will feed into policy making.
Make sure to to think about unintended consequences (e.g. export crop promotion shifts the intrahousehold allocation of power or S. Africa pensions) – qualitative and interdisciplinary perspectives are key here

43. Flexibility and adaptability
The evaluation must be tailored to the specific project and adapted to the specific institutional context.
The project design must be flexible to secure our ability to learn in a structured manner, feed evaluation results back into the project and change the project mid-course to improve project end results.
Can be broad project redesign or push in new directions e.g. feed into nutritional targeting design
This is an important point: In the past projects have been penalized for affecting mid-course changes in project design. Now we want to make change part of the project design.

44. But don’t be afraid to look at intermediate outcomes either
Horizon matters
The time it takes to achieve results is an important consideration for timing the evaluation. Conversely, the timing of the evaluation will determine what outcomes should be focused on.
Early evaluations should focus on outcomes that are quick to show change
For long-term outcomes, evaluations may need to span beyond project cycle. e.g. Indonesia school building project
Think through how things are expected to change over time and focus on what is within the time horizon for the evaluation
Do not confuse the importance of an outcome with the time it takes for it to change—some important outcomes are obtained instantaneously !
But don’t be afraid to look at intermediate outcomes either

45. Stage 1: Identification to PCN

46. Get an Early Start How do you get started?
Get help and access to resources: contact person in your region or sector responsible for impact evaluation and/or Thematic Group on Impact Evaluation
Define the timing for the various steps of the evaluation to ensure you have enough lead time for preparatory activities (e.g. baseline goes to the field before program activities start)
The evaluation will require support from a range of policy-makers: start building and maintaining constituents, dialogue with relevant actors in government, build a broad base of support, include stakeholders

47. Build the Team
Select impact evaluation team and define responsibilities of:
program managers (government),
WB project team, and other donors,
lead evaluator (impact evaluation specialist),
local research/evaluation team, and
data collection agency or firm
Selection of lead evaluator is critical for ensuring quality of product, and so is the capacity of the data collection agency
Partner with local researchers and research institutes to build local capacity

48. Shift Paradigm From a project design based on “we know what’s best”
To project design based on the notion that “we can learn what’s best in this context, and adapt to new knowledge as needed”
Work iteratively:
Discuss what the team knows and what it needs to learn–the questions for the evaluation—to deliver on project objectives
Discuss translating this into a feasible project design
Figure out what questions can feasibly be addressed
Housekeeping: Include these first thoughts in a paragraph in the PCN
e.g. ARV evaluation – funding constraints shifted radically, quickly – design changed, and changed again

49. Stage 2: Preparation through appraisal

50. Define project development objectives and results framework
This activity
clarifies the results chain (logic of impacts) for the project,
identifies the outcomes of interest and the indicators best suited to measure changes in those outcomes, and
the expected time horizon for changes in those outcomes.
This will provide the lead evaluator with the project specific variables that must be included in the survey questionnaire and a notion of timing for scheduling data collection.

51. Work out project design features that will affect evaluation design
Target population and rules of selection
This provides the evaluator with the universe for the treatment and comparison sample
Roll out plan
This provide the evaluation with a framework for timing data collection and, possibly, an opportunity to define a comparison group
Think about non-objective undermining changes that will enhance the evaluation (and this will likely be iterative)

52. Narrow down the questions for the evaluation
Questions aimed at measuring the impact of the project on a set of outcomes, and
Questions aimed at measuring the relative effectiveness of different features of the project

53. Questions aimed at measuring the impact of the project are relatively straightforward
What is your hypothesis? (Results framework)
By expanding water supply, the use of clean water will increase, water borne disease decline, and health status will improve
What is the evaluation question?
Does improved water supply result in better health outcomes?
How can do you test the hypothesis?
The government might randomly assign areas for expansion in water supply during the first and second phase of the program
What will you measure?
Measure the change in health outcomes in phase I areas relative to the change in outcomes in phase II areas. Outcomes will include use of safe water (S-T), incidence of diarrhea (S/M-T), and health status (L-T, depending on when phase II occurs). Add other outcomes.
What will you do with the results?
If the hypothesis proves true go to phase II; if false, modify policy.

54. require identifying the tough design choices on the table…
Questions aimed at measuring the relative effectiveness of different project features
require identifying the tough design choices on the table…
What is the issue?
What is the best package of products or services?
Where do you start from (what is the counterfactual)?
What package is the government delivering now?
Which changes do you or the government think could be made to improve effectiveness?

55. What will you do with the results?
How do you test it?
The government might agree to provide a package to a randomly selected group of households and another package to another group of households to see how the two package perform
What will you measure?
The average change in relevant outcomes for households receiving one package versus the same for households receiving the other package
e.g. extension vs fertilizer+extension vs fertilizer+extension+seeds
What will you do with the results?
The package that is most effective in delivering desirable outcomes becomes the one adopted by the project from the evaluation onwards

56. Application, features that should be tested early on
Early testing of project features (say 6 months to 1 year) can provide the team with the information needed to adjust the project early on in the direction most likely to deliver success.
Features might include:
alternative modes of delivery (e.g. use seed merchants vs. extension agents),
alternative packages of outputs, or
different pricing schemes (e.g. alternative subsidy levels).

57. Develop identification strategy (to identify the impact of the project separately from changes due to other causes )
One the questions are defined, the lead evaluator selects one or more comparison groups against which to measure results in the treatment group.
The “rigor” with which the comparison group is selected will determine the reliability of the impact estimates.
Rigor?
More-same observables and unobservables (experimental),
Less-same observables (non-experimental)

58. Explore Existing Data
Explore what data exists that might be relevant for use in the evaluation.
Discuss with the agencies of the national statistical system and universities to identify existing data sources and future data collection plans.
Check DECDG website
Record data periodicity, quality, variables covered and sampling frame and sample size, for
Censuses
Surveys (household, firms, facility, etc)
Administrative data
Data from the project monitoring system

59. New Data Start identifying additional data collection needs.
Data for impact evaluation must be representative of treatment and comparison group
Questionnaires must include outcomes of interest (consumption, income, assets etc), questions about the program in question and questions about other programs, as well as control variables
The data might be at household, community, firm, facility, or farm levels and might be combined with specialty data such as those from water or land quality tests.
Investigate synergies with other projects to combine data collection efforts and/or explore existing data collection efforts on which the new data collection could piggy back
Develop a data strategy for the impact evaluation including:
The timing for data collection
The variables needed
The sample (including size)
Plans to integrate data from other sources (e.g project monitoring data)

60. Prepare for collecting data
Identify data collection agency
Lead evaluator or team will work with the data collection agency to design sample, and train enumerators
Lead evaluator or team will prepare survey questionnaire or questionnaire module as needed
Pre-testing survey instrument may take place at this stage to finalize instruments
If financed with outside funds, baseline can now go to the field. If financed by project funds, baseline will go to the field just after effectiveness but before implementation starts

61. Develop a Financial Plan
Costs:
Lead evaluator and research/evaluation team,
Data collection,
Supervision and
Dissemination
Finances:
BB,
Trust fund,
Research grants,
Project funds, or
Other donor funds

62. Housekeeping
Initiate an IE activity. The IE code in SAP is a way of formalizing evaluation activities. The IE code recognizes the evaluation as a separate AAA product.
Prepare concept note
Identify peer reviewers –impact evaluation and sector specialist
Carry out review process
Appraisal documents
Include in the project description plans to modify project overtime to incorporate results
Work the impact evaluation into the M&E section of the PAD and Annex 3
Include the impact evaluation in the Quality Enhancement Review (TTL).

63. Stage 3: Negotiations to Completion

64. Ensure timely implementation
Ensure timely procurement of evaluation services especially contracting the data collection, and
Supervise timely implementation of the evaluation including
Data collection
Data analysis
Dissemination and feedback

65. Data collection agency/firm
Data collection agency or firm must have technical knowledge and sufficient logistical capacity relative to the scale of data collection required
The same agency or firm should be expected to do baseline and follow up data collection (and use the same survey instrument)

66. Baseline data collection and analysis
Baseline data collection should be carried out before program implementation begins; optimally even before program is announced
Analysis of baseline data will provide program management with additional information that might help finalize program design

67. Follow-up data collection and analysis
The timing of follow-up data collection must reflect the learning strategy adopted
Early data collection will help modifying programs mid course to maximize longer-term effectiveness
Later data collection will confirm achievement of longer-term outcomes and justify continued flows of fiscal resources into the program

68. Watch implementation closely from an evaluation point of view
Watch (monitor) what is actually being implemented:
Will help understand results of evaluation
Will help with timing of evaluation activities
Watch for contamination in the control group
Watch for violation of eligibility criteria
Watch for other programs for the same beneficiaries
Look for unintended impacts
Look for unexploited evaluation opportunities
 Good evaluation team communication is key here

69. Dissemination
Implement plan for dissemination of evaluation results ensuring that the timing is aligned with government’s decision making cycle.
Ensure that results are used to inform project management and that available entry points are exploited to provide additional feedback to the government
Ensure that wider dissemination takes place only after the client has had a chance to preview and discuss the results
Nurture collaboration with local researchers throughout the process

70. Housekeeping
Put in place arrangements to procure the impact evaluation work and fund it on time
Use early results to inform mid-term review
Use later results to inform the ICR, CAS and future operations

71. Summing up: Practicalities
Making evaluation work for you requires a change in the culture of project design and implementation, one that maximizes the use of learning to change course when necessary and improve the chances for success
Impact evaluation is more than a tool – it is an organizing analytical framework for doing this – it is not about measuring success or failure so much as it is about learning…

72. Where to go for assistance / more information
Clinics
Brochure here, PREM
TG resources
Searchable database of evaluations
Searchable roster of consultants
Doing IE series – general and sector notes
Website (
Courses – workshop on IE, WBI training, PAL course
South Asia resources: Jishnu Das (12/06)

73. Thank you