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1
Impact Evaluation: The case of Bogotá’s concession schools
Felipe Barrera-OsorioWorld Bank
October 2010
2
Outline
1. Hypotheses2. Detour: Evaluation of Programs3. Empirical strategy4. The data5. Results
Why concessions (a PPP) may increase quality of education?
• An application of an already proven pedagogic model• Concession schools are financially stable, ensuring the
stability of the pedagogic model.• Freedom to choose (and fire) teachers and management
staff• Better infrastructure• Concession schools work actively with the families and the
community– “Affection deficit” and family problems are important issues in
these communities• Nutrition is an important variable in educational
outcomes
3
4
Three hypothesis
• Dropout rates are lower in concessions schools than in similar, public schools.
• Nearby schools have lower dropout rates than public schools outside the influence of concessions.
• Test scores are higher in concessions schools than in similar, public schools.
5
Detour: Evaluating Programs
What is Impact Evaluation?
• Impact evaluation is a set of methods to identify and quantify the causal impact of programs
• For example, What is the effect of a concession schools on drop-out rates and test scores?
Why are these effects difficult to estimate?
• The basic question: What would it happen in the absence of the program? (e.g., what is the contrafactual?)
• We need the same individual with and without the program– For example, we need to observe John attending one concession
school, and (the same) John attending a public school– …but it is impossible to observe the same individual in
both states!• Solution: “build” the correct contrafactual
– Find individuals who do not have the benefits of the program, but are very similar to the ones that have the program
– For example, find David who is very similar in all characteristics to John…
What is the problem?• In short, the problem is to find the correct comparison
(control) group– The control group and the treatment group should have
the same characteristics, observable and non-observable, before the beginning of the program
– External factors will affect in the same way control and treatment group
• An, usually, people self-select into programs, and therefore, the beneficiaries are (with high probability), different that the ones that did not enter into the program
The basic intuition: only data before and after the program
Y: Test scores
Timet = 0
before program
t = 1
after program
Impact
of the
program?
NO!
We need a contrafactual
Intervention
We need the right comparison groupY
Timet = 0
before program
t = 1
after program
Impact
of the
program
Intervention
Control
Treatment
Y: Test scores
The basic intuition 2: only data after the program….
Y
Timet = 0
before program
t = 1
after program
Impact
of the
Program?
Intervention
Control
Treatment
Y: Test scores
We need the right comparison group!Y
Timet = 0
before program
t = 1
after program
Impact
of the
Program?
NO!
At t=0, two groups were
very different…
Intervention
Control
Treatment
Y: Test scores
Four possibilities to find or construct the right control group
• Prospective evaluation– Randomization of benefits– Randomization of entry (phase-in approach)– Randomization of information (encouragement design)
• Retrospective evaluation– Regression discontinuity analysis– Instrumental variables– Differences in differences– Propensity and matching estimators
Randomization
• Lottery among individuals will separate the sample between winners and losers– Model of “over-subscription”
• A lottery will create homogenous control and treatment group: they will be very similar in all characteristics, observable and non-observable.
Regression discontinuity• When to use this method?
– The beneficiaries/non-beneficiaries can be ordered along a quantifiable dimension.
– This dimension can be used to compute a well-defined index or parameter.
– The index/parameter has a cut-off point for eligibility.– The index value is what drives the assignment of a
potential beneficiary to the treatment. (or to non-treatment)
Intuition
• The potential beneficiaries just above the cut-off point are very similar to the potential beneficiaries just below the cut-off point.
• We compare outcomes for individuals just above and below the cutoff point.
6065
7075
80O
utco
me
20 30 40 50 60 70 80Score
Regression Discontinuity Design - Baseline
Non-poor
Poor
6570
7580
Out
com
e
20 30 40 50 60 70 80Score
Regression Discontinuity Design - Post Intervention
Treatment effect
Difference in differences1. Estimating the impact of a “past” program2. We can try to find a “natural experiment” that allows us to
identify the impact of a policy. For example, An unexpected change in policy could be seen as “natural
experiment” A policy that only affects 16 year olds but not 15 year olds
3. We need to identify which is the group affected by the policy change (“treatment”) and which is the group that is not affected (“control”).
4. The quality of the control group determines the quality of the evaluation.
Intuition
• Find a group that did not receive the program• …with the same pattern of growth in the
outcome variable before the intervention– The two groups, treatment and control, have the
same profile before the intervention
Intuition: when it is right to use DD?
Timet = -1 t = 1
after program
Impact
of the
Program
Intervention
Control
t = 0
Slopes, before intervention
(between -1 and 0) were equal
Y: Test scores
Intuition: when it is right to use DD?
Timet = -1 t = 1
after program
Impact
of the
Program?
NO: wrong comparison
group
Intervention
Control
t = 0
Slopes, before intervention
(between -1 and 0) were different
Y: Test scores
Propensity and matching estimation
• Find the comparison group from a large survey• Each treatment will have a comparison that has
observable characteristics as similar as possible to the treated individuals
• This method assumes that there is not self-selection based on unobservable characteristics:– Selection is based on observable characteristics
How is this procedure done? Two steps
• Estimation of the propensity score: The propensity score is the conditional probability of receiving the treatment given the pre-treatment variables.– Estimate the probability of been treated based on the observable
characteristics• Estimation of the average effect of treatment given the
propensity score– match cases and controls with exactly the same
(estimated) propensity score;– compute the effect of treatment for each value of the
(estimated) propensity score– obtain the average of these conditional effects
25
…Back to Concessions
26
Empirical Strategy for Concessions: propensity and matching estimation
• Propensity score estimation:– “Find” controls that have similar characteristics as the
treatment one
• Matching estimation:– Once the controls are found, estimate impact:
)0,)(/()1,)(/(~
0~
1~
TXPYETXPYEIP
)()()/Pr( xfXPXT
27
The data
1. C100 and C600: Rich information about schools• For 1999 and 2003: Administrative personnel,
number and level of education of teachers, physicians and psychologists, students by grade and by age, students who failed a grade and dropped out.
• For 1999: infrastructure variables like computers, rooms, labs, etc.
2. ICFEX 2003: Standardized test scores• Test scores at the individual level• Some information at the individual level
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Table 1. Basic stadistics (public schools)
Mean Std. Dev. Mean Std. Dev.A. CHARACTERISTICSLocalization
1 0.10 0.30 0.12 0.332 0.01 0.10 0.00 0.003 0.05 0.22 0.00 0.004 0.06 0.24 0.00 0.005 0.15 0.36 0.12 0.326 0.04 0.19 0.00 0.007 0.30 0.46 0.44 0.508 0.12 0.32 0.13 0.349 0.17 0.38 0.19 0.40
Type of the school Male 0.07 0.26 0.00 0.00 Female 0.03 0.16 0.00 0.00 Coed 0.90 0.30 1.00 0.00
Type of secondary Academic 0.63 0.48 0.93 0.26 Technical 0.29 0.45 0.00 0.00 Both 0.07 0.26 0.07 0.26
Schedule Complete 0.03 0.18 1.00 0.00 Morning 0.40 0.49 0.00 0.00 Afternoon 0.14 0.35 0.00 0.00 Evening 0.43 0.49 0.00 0.00
Number of students Total school 2954.60 1376.76 987.98 90.17 Taking exam 122.15 79.16 65.33 9.98 Old for grade 47.28 45.15 12.11 7.03 Repeating grade 103.30 73.72 47.56 35.93 Ratio teacher/stud. 28.44 6.20 27.20 3.48
Year of education, teachers 16.07 2.28 16.02 0.99
Non-concession Concession
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Table 1. Basic stadistics (continuation)B. OUTCOMES
Dropout rates
Grade 1 0.040 0.05 0.020 0.02 Grade 2 0.031 0.04 0.023 0.02 Grade 3 0.026 0.04 0.019 0.02 Grade 4 0.026 0.03 0.015 0.02 Grade 5 0.020 0.33 0.018 0.02 Grade 6 0.071 0.09 0.021 0.02 Grade 7 0.040 0.04 0.025 0.02 Grade 8 0.038 0.04 0.032 0.03 Grade 9 0.039 0.05 0.036 0.03 Grade 10 0.033 0.04 0.038 0.04 Grade 11 0.012 0.02 0.021 0.05
Test scores Mathematics 42.08 5.13 41.68 4.77 Reading 50.99 7.13 51.04 6.79
Number of observations Students Max 36244 1056 Min 34218 1013
Schools 2790 17
Non-concession Concession
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Flavor of the (strong) result
Concessions reduced the dropout rate significantely
Concessions reduce the jump in the desertion in grade 6
31
Test scores:
Public schools have lower test scores
Concessions and public non-concessions are “similar”
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Table 2. Basic statistics, schools near concession versus public schools
A. CHARACTERISTICS OF SCHOOLS
1. "Before concessions" (1999) Mean Std.dev Mean Std.dev
Infrastructure Number of computers 17.82 16.75 24.96 22.69 Number of classrooms 21.40 10.58 22.24 12.93 Number of bathrooms 1.45 0.78 1.67 0.87 Number of sport facilities 1.78 0.54 2.59 1.52
Teachers Number of teachers 30.27 16.34 34.46 24.06 Number of admin. pers. 6.97 4.45 9.69 11.96 Number of physicians 0.06 0.24 0.45 1.10 Number of psychologists, etc 1.52 1.29 2.19 1.85 % teachers with second. educ 0.07 0.18 0.06 0.12 % teachers with college educ 0.60 0.23 0.60 0.62 % teachers with graduate. educ 0.25 0.17 0.34 0.34 Ration students / teachers 24.92 3.24 26.16 28.78
Dropout rates 0.09 0.07 0.07 0.06
Schools near a concession Schools far a way from concession
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2. Follow up (2003)Localization
1 0.00 0.05 0.10 0.302 0.00 0.00 0.01 0.103 0.00 0.00 0.05 0.224 0.04 0.19 0.06 0.245 0.20 0.40 0.15 0.366 0.00 0.00 0.04 0.197 0.15 0.36 0.30 0.468 0.45 0.50 0.12 0.329 0.16 0.37 0.17 0.38
Gender of the school Male 0.00 0.00 0.07 0.26 Female 0.00 0.00 0.03 0.16 Coed 1.00 0.00 0.90 0.30Type of secondary Academic 0.80 0.40 0.63 0.48 Technical 0.06 0.24 0.29 0.45 Both 0.14 0.35 0.07 0.26Schedule Complete 0.04 0.20 0.03 0.18 Morning 0.28 0.45 0.40 0.49 Afternoon 0.07 0.25 0.14 0.35 Evening 0.61 0.49 0.43 0.49Number of students Total school 2544.18 786.25 2954.60 1376.76 Taking exam 136.97 69.02 122.15 79.16 Old for grade 35.52 35.30 47.28 45.15 Repeating grade 114.93 88.69 103.30 73.72 Ratio teacher/stud. 31.42 2.56 28.44 6.20Year of education, teachers 16.49 0.73 16.07 2.28
Dropout rates
Grade 1 0.035 0.04 0.040 0.05 Grade 2 0.030 0.03 0.031 0.04 Grade 3 0.030 0.03 0.026 0.04 Grade 4 0.018 0.02 0.026 0.03 Grade 5 0.021 0.02 0.020 0.33 Grade 6 0.054 0.06 0.071 0.09 Grade 7 0.029 0.03 0.040 0.04 Grade 8 0.051 0.04 0.038 0.04 Grade 9 0.046 0.03 0.039 0.05 Grade 10 0.040 0.03 0.033 0.04 Grade 11 0.012 0.02 0.012 0.02
Number of schools 23 416
Schools near a concession Schools far a way from concession
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Dropout Results: Probit estimatesTable 3. Direct and indirect impact of concessions: effects over dropout rates
Probit estimatesDepended variable
Coefficient Std. Err. Coefficient Std. Err.
Total number of students -0.008 0.001 * -0.015 0.003 *Dispersion in the test -0.366 0.080 * -0.708 0.178 *Localization 4 13.472 8.162 *Localization 5 0.024 0.168 9.129 8.556 Localization 7 0.212 0.138 13.929 8.089 *Localization 8 -0.052 0.163 12.411 8.234 Localization 9 0.048 0.155 12.455 8.311 Schedule Evening -0.243 0.173 1.966 0.430 *Ration teacher / stud. -0.284 0.046 * 0.514 0.094 *Average year of educ. teac. -0.035 0.025 0.080 0.079 % of students repeating -1.723 0.351 * 1.407 0.502 *% of students old for grade 3.327 0.719 * 5.040 1.709 *Number of computers, 1999 0.027 0.020 Number of class rooms, 1999 0.053 0.038 Number of restrooms, 1999 -4.686 0.719 *Number of sport facilities, 1999 -1.723 0.260 *Number of teachers, 1999 -0.476 0.278 *Number of administrative p. 1999 0.202 0.050 *Number of psychologist, 1999 -0.069 0.187 Ration teacher / stud., 1999 0.059 0.038 % teachers with second., 1999 35.714 10.066 *% teachers with college, 1999 33.968 9.841 *% teachers with grad. st., 1999 25.912 8.898 *
Log likelihood -529 -231PseudoR2 0.1391 0.5714Number of obs. 2334 1217
Direct impact over dropout rate
Concession
Indirect impact over dropout rate
Schools near concession
35
Dropout results: impact
Matching
Varible: Dropout rate
Difference Treatment-ControlUnmatched -0,0130 -0,0069ATT -0,0173 -0,0082
Bootstrap statisticsRepetitions 1000 1000Standard error 0,0039 0,0096317Bias-corrected conf. interv. -0,0244 -0,0112 -0,0341 0,0004
Impact variable: Dropout rate Impact variable: Dropout rate
Direct impact over dropout rate Indirect impact over dropout rate
Matching:
• 10 nearest estimators, common support, balance groups
• Direct Effect: reduction in 1.7 points dropout rates
• Indirect Effect: reduction in 0.82 points
36
Test Results: Probit estimatesTable 4. Direct impact of concessions: effects over test scores (mathematics and reading)
Probit estimatesDepended variable
Coefficient Std. Err. Coefficient Std. Err.
Total number of students -0.018 0.001 * -0.018 0.001 *Dispersion in the test -0.351 0.033 * -0.197 0.029 *Localization 5 -0.441 0.079 * -0.542 0.080 *Localization 7 0.149 0.063 * 0.140 0.064 *Localization 8 0.073 0.075 0.079 0.076 Localization 9 -0.150 0.071 * -0.181 0.072 *Schedule Evening 0.102 0.072 0.068 0.073 Ration teacher / stud. -0.071 0.005 * -0.072 0.005 *Average year of educ. teac. -0.089 0.012 * -0.095 0.011 *% of students repeating -2.508 0.183 * -2.575 0.179 *% of students old for grade 19.165 0.779 * 18.789 0.774 *Constant 4.683 0.300 * 4.474 0.330 *
Log likelihood 2152.87 -2846.556Pseudo R2 0.2768 0.268Number of obs. 18629 18630
Direct impact over math tests Direct impact over reading tests
Concession Concession
37
Test results: impact
Matching
Variable: Test scores
Difference Treatment-ControlUnmatched -0.2099 0.6234ATT 0.9732 1.9364
Bootstrap statisticsRepetitions 100 100Standard error 0.522 0.748Bias-corrected conf. interv. 1.2684 1.4655 1.5501 2.8472
Impact variable: Mathematics Impact variable: Reading
Matching:
• 10 nearest estimators, common support, balance groups
• Effect over math test scores: improvement of 2.4%
• Effect over language test scores: improvement of 4%
38
Some conclusions (and some questions)
• Concessions delivers:– Strong evidence of differences in dropout rates– Some evidence of impact over test scores
• Colombian government is implementing the II stage of Concessions
• What makes concessions different from public?– Is it the work with families and community?– Freedom of hiring teachers? – Pedagogic method?
39
A discussion about the data
• Limits of the data:– Not a randomized experiment– Still some source of bias
• Ideally, we want to have more hard-evidence– Randomization of students who applied to
concessions
Why is this important?
• Impact evaluation can provide reliable estimates of the causal effects of programs
• Impact evaluation can potentially help improve the efficacy of programs by influencing design and implementation
• Impact evaluation can broaden political support for programs
• Impact evaluation can help in the sustainability of successful programs and the termination of programs that are a failure
• Impact evaluation can help expand our understanding of how social programs produce the effects that they do