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Measuring Impact: Impact Evaluation Methods for Policy Makers

Measuring Impact: Impact Evaluation Methods for Policy Makers. Paul Gertler UC Berkeley.

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Measuring Impact: Impact Evaluation Methods for Policy Makers

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  1. Measuring Impact:Impact Evaluation Methods for Policy Makers Paul Gertler UC Berkeley Note: slides by Sebastian Martinez, Christel Vermeersch and Paul Gertler. The content of this presentation reflects the views of the authors and not necessarily those of the World Bank. This version: November 2009.

  2. Impact Evaluation • Logical Framework • How the program works “in theory” • Measuring Impact • Identification Strategy • Data • Operational Plan • Resources

  3. Measuring Impact • Causal Inference • Counterfactuals • False Counterfactuals: • Before & After (pre & post) • Enrolled & Not Enrolled (apples & oranges) • IE Methods Toolbox: • Random Assignment • Random Promotion • Discontinuity Design • Difference in Difference (Diff-in-diff) • Matching (P-score matching)

  4. Our Objective • Estimate the CAUSAL effect (impact) of • intervention P (program or treatment) • on • outcome Y (indicator, measure of success) • Example: what is the effect of a • Health Insurance Subsidy Program(P) • on • Out of Pocket Health Expenditures (Y)?

  5. Causal Inference • What is the impact of P on Y? • Answer: α= (Y | P=1)-(Y | P=0) • Can we all go home?

  6. Problem of missing data For a program beneficiary: • we observe (Y | P=1): • Health expenditures (Y) with health insurance subsidy (P=1) • but we do not observe (Y | P=0): • Health expenditures (Y) without health insurance subsidy (P=0) α= (Y | P=1)-(Y | P=0)

  7. Solution • Estimate what would have happened to Y in the absence of P • We call this the………… COUNTERFACTUAL The key to a good impact evaluation is a valid counterfactual!

  8. OBSERVE (Y | P=1) Outcome with treatment ESTIMATE (Y | P=0) counterfactual Estimating Impact of P on Y α= (Y | P=1) - (Y | P=0) IMPACT = outcome with treatment - counterfactual • Intention to Treat (ITT) -Those offered treatment • Treatment on the Treated (TOT) – Those receiving treatment • Use comparison or control group

  9. Example: What is the Impact of: giving Fulanito additional pocket money (P) on Fulanito’s consumption of candies (Y)

  10. The perfect “Clone” Fulanito Fulanito’s Clone 6 Candies 4 Candies Impact =

  11. In reality, use statistics Treatment Comparison Average Y = 6 Candies Average Y = 4 Candies Impact = 6 - 4 = 2 Candies

  12. Finding Good Comparison Groups • We want to find “clones” for the Fulanito’s in our programs • The treatment and comparison groups should: • have identical characteristics, • except for benefiting from the intervention • In practice, use program eligibility & assignment rules to construct valid counterfactuals With a good comparison group, the only reason for different outcomes between treatments and controls is the intervention (P)

  13. Case Study: HISP • National Health System Reform • Closing gap in access and quality of services between rural and urban areas • Large expansion in supply of health services • Reduction of health care costs for rural poor • Health Insurance Subsidy Program (HISP) • Pilot program • Covers costs for primary health care and drugs • Targeted to poor – eligibility based on poverty index • Rigorous impact evaluation with rich data • 200 communities, 10,000 households • Baseline and follow-up data two years later • Many outcomes of interest • Yearly out of pocket health expenditures per capita • What is the effect of HISP (P) on health expenditures (Y)? • If impact is a reduction of $9 or more, then scale up nationally

  14. Case Study: HISP Eligibility and Enrollment Not Enrolled Enrolled

  15. Measuring Impact • Causal Inference • Counterfactuals • False Counterfactuals: • Before & After (pre & post) • Enrolled & Not enrolled (apples & oranges) • IE Methods Toolbox: • Random Assignment • Random Promotion • Discontinuity Design • Difference in Difference (Diff-in-diff) • Matching (P-score matching)

  16. Counterfeit Counterfactual #1 Before & After Y A IMPACT? C (counterfactual) B Time T=0 Baseline T=1 Endline

  17. Case 1: Before & After • Observe only beneficiaries (P=1) • 2 observations in time • expenditures at T=0 • expenditures at T=1 “Impact” = A-B = What is the effect of HISP (P) on health expenditures (Y)? Y B 14.4 α= A 7.8 Time T=0 T=1

  18. Case 1: Before & After Note: If the effect is statistically significant at the 1% significance level, we label the estimated impact with 2 stars (**).

  19. Case 1: What’s the Problem? Economic Boom: • Real Impact = A-C • A-B is an underestimate Economic Recession: • Real Impact = A-D • A-B is an overestimate Y C? 14.4 B Impact ? α= -$6.6 D? Impact ? 7.8 A Time T=0 T=1 Problem with before & after: doesn’t control for other time-varying factors!

  20. Measuring Impact • Causal Inference • Counterfactuals • False Counterfactuals: • Before & After (pre & post) • Enrolled & Not Enrolled (apples & oranges) • IE Methods Toolbox: • Random Assignment • Random Promotion • Discontinuity Design • Difference in Difference (Diff-in-diff) • Matching (P-score matching)

  21. False Counterfactual #2 Enrolled & Not Enrolled • If we have post-treatment data on • Enrolled: treatment group • Not-enrolled: “control” group (counterfactual) • Those ineligible to participate • Those that choose NOT to participate • Selection Bias • Reason for not enrolling may be correlated with outcome (Y) • Control for observables • But not unobservables!! • Estimated impact is confounded with other things

  22. Case 2: Enrolled & Not Enrolled Measure outcomes in post-treatment (T=1) Not Enrolled Y = 21.8 Enrolled Y = 7.8 In what ways might enrolled & not enrolled be different, other than their enrollment in the program?

  23. Case 2: Enrolled & Not Enrolled Note: If the effect is statistically significant at the 1% significance level, we label the estimated impact with 2 stars (**).

  24. Policy Recommendation? • Will you recommend scaling up HISP? • Before-After: • Are there other time-varying factors that also influence health expenditures? • Enrolled-Not Enrolled: • Are reasons for enrolling correlated with health expenditures? • Selection Bias

  25. Keep in mind…….. • Two common comparisons to be avoided!! • Before & After (pre & post) • Compare: same individuals before and after they receive P • Problem: other things may have happened over time • Enrolled & Not Enrolled (apples & oranges) • Compare: a group of individuals that enrolled in a program with a group that chooses not to enroll • Problem: Selection Bias  we don’t know why they are not enrolled • Both counterfactuals may lead to biased estimates of the impact

  26. Measuring Impact • Causal Inference • Counterfactuals • False Counterfactuals: • Before & After (pre & post) • Enrolled & Not Enrolled (apples & oranges) • IE Methods Toolbox: • Random Assignment • Random Promotion • Discontinuity Design • Difference in Differences (Diff-in-diff) • Matching (P-score matching)

  27. Choosing your IE method(s)….. • Key information you will need for identifying the right method for your program: • Prospective/retrospective evaluation? • Eligibility rules and criteria? • Poverty targeting? • Geographic targeting ? • Roll-out plan (pipeline) ? • Is the number of eligible units larger than available resources at a given point in time? • Budget and capacity constraints? • Excess demand for program? • Etc….

  28. Choosing your IE method(s)….. • Best design = best comparison group you can find + least operational risk • Have we controlled for “everything”? • Internal validity • Good comparison group • Is the result valid for “everyone”? • External validity • Local versus global treatment effect • Evaluation results apply to population we’re interested in Choose the “best” possible design given the operational context

  29. Measuring Impact • Causal Inference • Counterfactuals • False Counterfactuals: • Before & After (pre & post) • Enrolled & Not enrolled (apples & oranges) • IE Methods Toolbox: • Random Assignment • Random Promotion • Discontinuity Design • Difference in Differences (Diff-in-diff) • Matching (P-score matching)

  30. Randomized Treatments and Controls • When universe of eligibles > # benefits: • Randomize! • Lottery for who is offered benefits • Fair, transparent and ethical way to assign benefits to equally deserving populations • Oversubscription: • Give each eligible unit the same chance of receiving treatment • Compare those offered treatment with those not offered treatment (controls) • Randomized phase in: • Give each eligible unit the same chance of receiving treatment first, second, third…. • Compare those offered treatment first, with those offered treatment later (controls)

  31. Randomized treatments and controls 3. Randomize Treatment 1. Universe 2. Random Sample of Eligibles Control External Validity Internal Validity Ineligible = Eligible =

  32. Unit of Randomization • Choose according to type of program: • Individual/Household • School/Health Clinic/catchment area • Block/Village/Community • Ward/District/Region • Keep in mind: • Need “sufficiently large” number of units to detect minimum desired impact  power • Spillovers/contamination • Operational and survey costs As a rule of thumb, randomize at the smallest viable unit of implementation

  33. Case 3: Random Assignment • Health Insurance Subsidy Program (HISP) • Unit of randomization: Community • 200 communities in the sample • Randomized phase-in: • 100 treatment communities (5,000 households) • Started receiving transfers at baseline T = 0 • 100 control communities (5,000 households) • Receive transfers after follow up T = 1 if program is scaled up

  34. Case 3: Random Assignment T=0 T=1 Time 100 Treatment Communities (5,000 HH) 100 Control Communities (5,000 HH) Comparison period

  35. Case 3: Random Assignment How do we know we have good clones?

  36. Case 3: Balance at Baseline **= significant at 1%

  37. Case 3: Balance at Baseline **= significant at 1%

  38. Case 3: Random Assignment Note: If the effect is statistically significant at the 1% significance level, we label the estimated impact with 2 stars (**).

  39. HISP Policy Recommendation? **= significant at 1%

  40. Keep in mind…….. • Random Assignment: • With large enough samples, produces two groups that are statistically equivalent • We have identified the perfect “clone” • Feasible for prospective evaluations with over-subscription/excess demand • Most pilots and new programs fall into this category! Randomized beneficiary Randomized comparison

  41. Remember….. • Objective of impact evaluation is to estimate the CAUSAL effect or IMPACT of a program on outcomes of interest • To estimate impact, we need to estimate the counterfactual • What would have happened in the absence of the program • Use comparison or control groups • We have toolbox with 5 methods to identify good comparison groups • Choose the best evaluation method that is feasible in the program’s operational context

  42. THANK YOU!

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