Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.

Gilligan quantitative impact eval methods


Published on

Published in: Technology, Health & Medicine
  • Be the first to comment

  • Be the first to like this

Gilligan quantitative impact eval methods

  2. 2. An Introduction to Quantitative Impact Evaluation I. Why is impact evaluation important? • What are appropriate goals for an impact evaluation? • Monitoring and evaluation II. How do you design an impact evaluation? • The evaluation problem • Measuring causal impact • Impact evaluation methodologies
  3. 3. Introduction (cont‟d) III. Impact Evaluation and Measurement Tools • Choice of evaluation estimator • Data requirements • How to randomize • Sample design • Sample size
  4. 4. What are appropriate goals for an impact evaluation?  Measure impact on important outcomes • Need a limited set of outcome indicators that are easy to measure  Estimate the program‟s cost effectiveness  Explain which components of a program work best  Caution: • Evaluations can only answer a limited number of questions • Evaluations sometimes cannot explain what caused the impacts  Effective monitoring and qualitative assessments help to explain the context for impact evaluation results
  5. 5. Indicators for Monitoring and Evaluation IMPACT OUTPUTS OUTCOMES INPUTS Effect on living standards -better welfare impacts (e.g literacy, health) - increase in participation, happiness Financial and physical resources - track resources used in the intervention - e.g. budget support for local service delivery Goods and services generated - more local government services delivered - e.g., textbooks, food delivered, roads built Access, usage and satisfaction of users - e.g. school attendance, vaccination rates, - food consumption, number of mobile phones EvaluationMonitoring
  6. 6. II. How do you design an impact evaluation?  The central problem of impact evaluation • Want to measure the impact of a program or “treatment” on outcomes • How do we know measured impacts are due to the program? • If we want to claim that the impacts observed are causal, we need an „identification strategy‟—a way to attribute the observed effects to the program and not to other factors
  7. 7. II. How do you design an impact evaluation?  Designing the impact evaluation • Measure impact by comparing outcomes in households exposed to the treatment to what those outcomes would have been without that exposure—the counterfactual • Problem: you cannot observe the counterfactual because program beneficiaries receive the treatment • Need to construct a comparison group from nonbeneficiaries • Comparison group makes it possible to control for other factors that affect the outcome  Ex: IFPRI evaluated the effect of Ethiopia‟s public works (PSNP) on food consumption, but food prices rose at the same time; use comparison group to remove the effect of rising prices on food consumption in impact estimates
  8. 8. Suppose we observe an increase in outcome Y for beneficiaries over time after an intervention Y0 Y1 baseline(t0) follow-up(t1) Intervention (observed)
  9. 9. To measure impact, we need to remove the counterfactual from the observed outcome Y0 Y1 baseline(t0) follow-up(t1) Intervention (observed) Y1 * Impact= Y1-Y1 * (counterfactual) Comparison
  10. 10. What You Can Miss Without a Comparison Group 0.0 5.0 10.0 15.0 20.0 25.0 30.0 35.0 SFP THR CTR % Round 1 Round 2 -3.4 13.9 -5.3 Impact: SFP -19.2% THR -17.2% (*Anemic = hemoglobin<11g/dL) Impact on School Feeding on Anemia Prevalence of Girls Age 10-13
  11. 11. Constructing a Comparison Group  Suppose we want to measure the impact of public works on household food security (calorie consumption)  Q: Why not compare average calorie consumption of PW beneficiaries to average calorie consumption of randomly selected nonbeneficiaries?  A: On average, nonbeneficiaries are different from beneficiaries in ways that make them an ineffective comparison group  Need to correct for pre-program differences between beneficiaries and nonbeneficiaries • Beneficiaries are usually poorer; they also decided to participate • If you don‟t control for this, impact estimates are biased
  12. 12. Impact Evaluation Methodologies Ways of constructing a control or comparison group  Randomization  Matching (including propensity score matching, covariate matching)  Regression discontinuity design (RDD)  Instrumental variables  Difference-in-differences
  13. 13. Impact Evaluation Methodologies Randomization • Randomly assign communities or households into treatment and control groups before the program for the purpose of evaluation  random assignment makes it likely that treatment and control communities have identical characteristics on average at baseline  for safety nets, usually randomize at the community level • Common approach: use phased rounds of program implementation and randomly decide which communities enter the program in each round • Example of randomization from N. Uganda school feeding study
  14. 14. Impact Evaluation Methodologies Randomization • How do you justify having a control group?  Justified if program cannot reach all communities at once  Some communities are always excluded  Main difference between control group and other nonbeneficiaries is that you interview the control group  Ex: transparency in Nicaragua RPS evaluation. Randomization done in public with media and politicians present • There is consensus that a randomized out control group provides the best estimate of counterfactual outcomes  Results of a good randomized evaluation will be convincing to everyone: you have solid evidence of the impact of the program
  15. 15. Impact Evaluation Methodologies Matching • Match beneficiary and nonbeneficiary households by characteristics observed in a survey • Estimate impact as the difference in weighted average outcomes between beneficiaries and matched nonbeneficiaries • Propensity score matching matches households on estimated probability of being in the program • With matching, the quality of the evaluation depends heavily on the quality of the data: not as convincing as randomization
  16. 16. Propensity Score Matching 0 .5 1 1.5 2 0 .2 .4 .6 .8 1 Estimated propensity score Non-beneficiary Beneficiary Kernel density of PPS by treatment status
  17. 17. Impact Evaluation Methodologies Many of the projects being presented here may be able to rely on matching methods for their evaluation • Need detailed data from the baseline or on variables that change very little over time (adult education level) Tips on Using Propensity Score Matching • Need variables that are correlated with the outcome and with the treatment • Comparison households should come from the same community as treated households if possible; otherwise include many community-level variables
  18. 18. Impact Evaluation Methodologies Regression Discontinuity Design (RDD)  If program eligibility is based on threshold for some characteristic (e.g., poverty index), compare outcomes for households just above and just below the threshold  More useful for poverty programs targeted on easily observable and measureable criteria » poverty score, proxy means score, food insecurity score
  19. 19. How RDD Measures Impact  Before start of the program 0510152025 20 25 30 35 40 45 Poverty Score Pr(CompleteSecondarySchool)
  20. 20. How RDD Measures Impact  After the program 0510152025 20 25 30 35 40 45 Poverty Score Pr(CompleteSecondarySchool) beneficiariesnonbeneficiaries
  21. 21. How RDD Measures Impact  After the program 0510152025 20 25 30 35 40 45 Poverty Score Pr(CompleteSecondarySchool) beneficiariesnonbeneficiaries IMPACT
  22. 22. Example of RDD from El Salvador RPS Evaluation  Figure 4. Change in enrollment rate of 7-12 year olds from 2006-2007 by distance from implied cluster threshold, 2006 and 2007 entry groups  Source: Impact Evaluation Survey Data, 2008 -.05 0 .05 .1 ChangeinEnrollmentRate -10 -5 0 5 10 15 Distance to Cluster Threshold 2006 2007
  23. 23. Difference-in-Differences (DID)  Using any evaluation method, measure outcomes before and after the program begins to obtain “difference-in-differences” (DID) impact estimates Impact = (T1-T0)-(C1-C0)
  24. 24. Cost Effectiveness  Comparisons of programs should focus on cost effectiveness. • Cost effectiveness is most relevant for policy: Which program has the biggest impact per dollar spent? • Impact evaluation methodology focuses on measuring program benefits—one side of cost effectiveness.  Would need to add a cost study similar to Caldés, Coady and Maluccio, IFPRI, 2004.