Lect2-Worms

Lect2-Worms - Randomized Experiment: De-worming in Kenya...

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Unformatted text preview: Randomized Experiment: De-worming in Kenya Estimation Next Time Lect 2: Health and Introduction to Randomization Nancy Qian January 12, 2011 Nancy Qian Econ 404/776: Lecture 2 Randomized Experiment: De-worming in Kenya Estimation Next Time Agenda 1 Randomized Experiment: De-worming in Kenya The Basic Problem Baseline Survey Sample Attrition Bundled Treatments Comparison Group 2 Estimation Compliance Derivation Results 3 Next Time Nancy Qian Econ 404/776: Lecture 2 Randomized Experiment: De-worming in Kenya Estimation Next Time Basic Problem Baseline Survey Sample Attrition Bundled Treatments Comparison Group Outline 1 Randomized Experiment: De-worming in Kenya The Basic Problem Baseline Survey Sample Attrition Bundled Treatments Comparison Group 2 Estimation Compliance Derivation Results 3 Next Time Nancy Qian Econ 404/776: Lecture 2 Randomized Experiment: De-worming in Kenya Estimation Next Time Basic Problem Baseline Survey Sample Attrition Bundled Treatments Comparison Group Program Evaluation Question: What are the effects of de-worming on school attendance? To address the question of whether health has a causal impact on schooling, Miguel and Kremer (2004) randomly assigned deworming drugs (across schools) to children in Kenya. The PSDP project introduced de-worming drugs into 3 groups of schools. Groups 1& 2 will receive the drug and Group 3 is the control group The unit of randomization is at the school level Nancy Qian Econ 404/776: Lecture 2 Randomized Experiment: De-worming in Kenya Estimation Next Time Basic Problem Baseline Survey Sample Attrition Bundled Treatments Comparison Group Why Randomize? Why not just control for factors? Are there pitfalls to randomization? Why randomize across schools? Nancy Qian Econ 404/776: Lecture 2 Randomized Experiment: De-worming in Kenya Estimation Next Time Basic Problem Baseline Survey Sample Attrition Bundled Treatments Comparison Group Outline 1 Randomized Experiment: De-worming in Kenya The Basic Problem Baseline Survey Sample Attrition Bundled Treatments Comparison Group 2 Estimation Compliance Derivation Results 3 Next Time Nancy Qian Econ 404/776: Lecture 2 Randomized Experiment: De-worming in Kenya Estimation Next Time Basic Problem Baseline Survey Sample Attrition Bundled Treatments Comparison Group Baseline Survey MK check the baseline characteristics by comparing the characteristics of the students in the control and treatment groups before the program begins Why is this important? What do you expect to see? See Table I Baseline Characteristics Baseline characteristics should be similar between samples. Nancy Qian Econ 404/776: Lecture 2 Randomized Experiment: De-worming in Kenya Estimation Next Time Basic Problem Baseline Survey Sample Attrition Bundled Treatments Comparison Group Descriptive Statistics One of the biggest mistakes empirical economists make is to not closely examine the descriptive statistics Many benefits Check randomization Check data quality Check that trends and patterns are realistic See means – why is this important? Check that there is variation – why is this important? Nancy Qian Econ 404/776: Lecture 2 Randomized Experiment: De-worming in Kenya Estimation Next Time Basic Problem Baseline Survey Sample Attrition Bundled Treatments Comparison Group Outline 1 Randomized Experiment: De-worming in Kenya The Basic Problem Baseline Survey Sample Attrition Bundled Treatments Comparison Group 2 Estimation Compliance Derivation Results 3 Next Time Nancy Qian Econ 404/776: Lecture 2 Randomized Experiment: De-worming in Kenya Estimation Next Time Basic Problem Baseline Survey Sample Attrition Bundled Treatments Comparison Group Sample Attrition One problem in administering the program was that they could only give students medication if students came to school that day Poorer students who attend school less will be less likely to receive treatment Look at Table III shows that in treatment schools. How many students in the treatment group receive treatment? In the untreated group? 70% of the students receive treatment Nancy Qian Econ 404/776: Lecture 2 Randomized Experiment: De-worming in Kenya Estimation Next Time Basic Problem Baseline Survey Sample Attrition Bundled Treatments Comparison Group Does this pose a problem for the interpretation? Nancy Qian Econ 404/776: Lecture 2 Randomized Experiment: De-worming in Kenya Estimation Next Time Basic Problem Baseline Survey Sample Attrition Bundled Treatments Comparison Group Yes & No: The comparison will not tell us the effect of the program on the population at large It will tell us the effect of the program on students who receive the drugs As long as the same population of absent students exist in the control group schools Nancy Qian Econ 404/776: Lecture 2 Randomized Experiment: De-worming in Kenya Estimation Next Time Basic Problem Baseline Survey Sample Attrition Bundled Treatments Comparison Group Outline 1 Randomized Experiment: De-worming in Kenya The Basic Problem Baseline Survey Sample Attrition Bundled Treatments Comparison Group 2 Estimation Compliance Derivation Results 3 Next Time Nancy Qian Econ 404/776: Lecture 2 Randomized Experiment: De-worming in Kenya Estimation Next Time Basic Problem Baseline Survey Sample Attrition Bundled Treatments Comparison Group Bundled Treatments In addition to de-worming medication, students at treatment schools received health classes on cleanliness and sanitation What problem does this pose for the interpretation of the results? How do you propose to resolve it? Nancy Qian Econ 404/776: Lecture 2 Randomized Experiment: De-worming in Kenya Estimation Next Time Basic Problem Baseline Survey Sample Attrition Bundled Treatments Comparison Group Outline 1 Randomized Experiment: De-worming in Kenya The Basic Problem Baseline Survey Sample Attrition Bundled Treatments Comparison Group 2 Estimation Compliance Derivation Results 3 Next Time Nancy Qian Econ 404/776: Lecture 2 Randomized Experiment: De-worming in Kenya Estimation Next Time Basic Problem Baseline Survey Sample Attrition Bundled Treatments Comparison Group Comparison Group What is the right comparison group? why? Those who took the pill versus those who did not? Those who took the pills versus the comparison group? All of those initially in the treatment group (all of those who were supplosed to take the pills) versus all of those assigned to the comparison groups? This comparison is called the Intention to Treat (ITT) estimate How do we obtain the average effect? Attrition What would happen to the sample if the treatment students were much healthier because of the experiment and the comparion group saw no improvement? Nancy Qian Econ 404/776: Lecture 2 Randomized Experiment: De-worming in Kenya Estimation Next Time Compliance Derivation Results Estimation Denote school attendance Y receiving the de-worming pill P assignment into treatment T assignment into control C If compliance was 100%, then the effect of deworming on school attendance is simply the ITT E [Y |T ] − E [Y |C ] But compliance is not perfect. Table III shows that compliance is around 80% How do we take this into account? Nancy Qian Econ 404/776: Lecture 2 Randomized Experiment: De-worming in Kenya Estimation Next Time Compliance Derivation Results Outline 1 Randomized Experiment: De-worming in Kenya The Basic Problem Baseline Survey Sample Attrition Bundled Treatments Comparison Group 2 Estimation Compliance Derivation Results 3 Next Time Nancy Qian Econ 404/776: Lecture 2 Randomized Experiment: De-worming in Kenya Estimation Next Time Compliance Derivation Results Compliance We can scale the ITT by the compliance to treatment E [Y |T ] − E [Y |C ] E [pill |T ] − E [pill |C ] It is easy to see that in the perfect experiment, E [pill |T ] − E [pill |C ] = 1 Nancy Qian Econ 404/776: Lecture 2 Randomized Experiment: De-worming in Kenya Estimation Next Time Compliance Derivation Results Outline 1 Randomized Experiment: De-worming in Kenya The Basic Problem Baseline Survey Sample Attrition Bundled Treatments Comparison Group 2 Estimation Compliance Derivation Results 3 Next Time Nancy Qian Econ 404/776: Lecture 2 Randomized Experiment: De-worming in Kenya Estimation Next Time Compliance Derivation Results Derivation To see how we address imperfect compliance, assume the following linear relationship Y = C + αP + ε And that the probability of taking a pill is the probability of taking the pill absent the treatment plus the probability of taking the pill conditional on being treated. K + γT + η Then E [Y |T ] = E [C + α P + ε |T ] = E [C + α P |T ] = E [C + α (K + γ T + η )|T ] = C + αγ Nancy Qian Econ 404/776: Lecture 2 Randomized Experiment: De-worming in Kenya Estimation Next Time Compliance Derivation Results Why did ε |T and η |T disappear? Why did α K |T disappear? Nancy Qian Econ 404/776: Lecture 2 Randomized Experiment: De-worming in Kenya Estimation Next Time Compliance Derivation Results Why did ε |T and η |T disappear? Treatment is uncorrelated with error term due to randomization Why did α K |T disappear? MK show that no one received treatment before the intervention Nancy Qian Econ 404/776: Lecture 2 Randomized Experiment: De-worming in Kenya Estimation Next Time Compliance Derivation Results Wald Estimator Similarly, using Table III shows that there are no pill recipients in the control group E [Y |C ] = E [C + α P + ε |C ] = C + E [ε |C ] = C Hence the ITT E [Y |T ] − E [Y |C ] = αγ ˆ α= ˆ ITT ˆ γ Wald Estimator ˆ α is called the Wald Estimator , a binary 2SLS or instrumental variables estimator. Nancy Qian Econ 404/776: Lecture 2 Randomized Experiment: De-worming in Kenya Estimation Next Time Compliance Derivation Results Outline 1 Randomized Experiment: De-worming in Kenya The Basic Problem Baseline Survey Sample Attrition Bundled Treatments Comparison Group 2 Estimation Compliance Derivation Results 3 Next Time Nancy Qian Econ 404/776: Lecture 2 Randomized Experiment: De-worming in Kenya Estimation Next Time Compliance Derivation Results Results Table V shows the results Nancy Qian Econ 404/776: Lecture 2 Randomized Experiment: De-worming in Kenya Estimation Next Time Compliance Derivation Results Externalities Outside Schools Reducing the worm load of a student can reduce the probability of infection of those near him, in school and outside of school. How does Miguel and Kremer estimate the externality effect? How would you test this? What if you were able to design an experiment, what would you do? What do they find? Cost-benefit: Including the externality benefits, the cost per additional year of school participation is only $3.50 Nancy Qian Econ 404/776: Lecture 2 Randomized Experiment: De-worming in Kenya Estimation Next Time Next Time More on reduced form estimation and identification. Acemoglu and Johnson (2007) and Bleakeley (2007) Nancy Qian Econ 404/776: Lecture 2 ...
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This note was uploaded on 08/12/2011 for the course ECON 404 taught by Professor Nancyqian during the Spring '11 term at Yale.

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