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Guest Lecture Guest Lecture Patricio S. Dalton Tilburg University Centre for Development Studies University of Glasgow 7 March 2019 Who am I? - Born and raised in Argentina. Big supporter of San Lorenzo (Buenos Aires football team) - 18 years ago I moved to Europe - Studied MSc and PhD in Economics at Warwick (met Theo Koutmeridis and Sayantan Ghosal there) - Associate Professor in Tilburg University, The Netherlands - Research on Behavioral Development Economics & Psychology of Poverty - Theory: Poverty and aspirations, welfare implications of bounded rationality - Lab Experiments: Goals, rationality, stress, hormones, self-confidence, generosity - Lab-in-the-field experiments: Unemployment benefits (Colombia) - RCTs: - Mobile money as payment instrument (Kenya) - Goal setting and productivity (Ghana) - Business practices and aspirations (Indonesia) - Financial worries and risk preferences (Vietnam) - Empowerment and accountability (India) Structure of today’s lecture Block 1: Conceptual overview of RCTs • What? • Why? • How? • Main challenges Block 2: Practical example of an RCT • Dalton, Ruschenpohler, Uras, Zia (2018) “Learning Best Practices from Peers”, Working paper. References • Banerjee and Duflo (2009). “The Experimental Approach to Development Economics”, Annual Review of Economics, 1:151–78 • Bruhn and McKenzie (2009). “In Pursuit of Balance: Randomization in Practice in Development Field Experiments” American Economic Journal: Applied, 1:4, 200–232 • Duflo, Glennerster and Kremer (2008) “Using Randomization in Development Economics Research: A Toolkit” T. Schultz and John Strauss, eds., Handbook of Development Economics. Vol. 4. Amsterdam and New York: North Holland. • Harrison and List (2004) “Field Experiments”, Journal of Economic Literature, 42:4, 1009-1055. • Imbens and Angrist (1994). “Identification and Estimation of Local Average Treatment Effects.” Econometrica, 62:2, 467-475 • List (2011) “Why Economists Should Conduct Field Experiments and 14 Tips for Pulling One Off”, Journal of Economic Perspectives: 25:3, 3-16 Why RCTs? Dogs, sunrise, umbrellas and rain Observation I: Every morning I let my dog out Observation II: When people use umbrellas, and then shortly after the sun comes up. it rains Do dogs make the sun rise? Do umbrellas make it rain? Policy recommendation: Distribute umbrellas in regions with drought! Correlation Fallacy Correlation fallacy: the logical mistake of believing that because two events occurred together, there is a cause-effect relationship. • People who play a musical instrument have higher IQ. playing an instrument increases IQ? people with high IQ play an instrument? • People who speak more languages are wealthier. Wealthier people study more languages? Speaking more languages wealthier? Policy implications are very different! Messerli (2012) “Chocolate Consumption, Cognitive Function, and Nobel Laureates” N. Engl J Med, 367:1562-1564 The Problems of Causal Inference • Causal impact counterfactual • What is the effect of a college degree on future earnings? • How much you guys would earn without a college degree? • How much people who do not have a college degree would earn if they had one? • Comparing people over time (before and after) will not give us, in most cases, a reliable estimate of impact. Why? 1. Unobserved factors affecting earnings may (and will) change while and after receiving the education. 2. Going to college is a decision influenced by you or others. Individuals who go to school may differ from those who do not go, in aspects that also affect future earnings Selection bias Selection Bias • Selection bias arises when individuals are selected (or self-selected) for treatment based on (typically unobserved) characteristics that may also affect their outcomes. Positive: Those who come to school are more motivated overestimate the effect Negative: Those who come to school are less motivated underestimate the effect • Difficult to disentangle the impact of the treatment from the factors that drove selection. • Selection bias is a problem endemic to retrospective evaluation • Many econometric ways of addressing this problems. For example: • find an instrument Z for the endogenous variable X (IV approach) – 푪풐풓 푿, 풁 ≠ ퟎ – 푪풐풓 풁, 훆 = ퟎ • Assign X randomly (experiment) – Addresses the endogeneity caused by “unobserved factors” and “selection-bias” creates a counterfactual by design Types of experiments (Harrison and List, 2004) • Conventional lab experiment: students, abstract framing, imposed set of rules • Artefactual field experiment: IDEM conventional but with non-student subject pool • Framed field experiment (or lab-in-the-field): IDEM artefactual but with field context • Natural field experiment (or RCT): IDEM framed but where the environment is one where the subjects naturally undertake these tasks and where the subjects do not know that they are in an experiment. • RCTs: • To evaluate the impact of existing programs • To test economic model predictions Practicalities of RCTs Overview: Understanding and Analyzing RCTs 1. Ethics Review Board approval (ERB) & Pre-analysis plan registration 2. Timeline of an RCT and Data 3. Randomization • How we actually do the randomization? • Simple vs stratified 4. Analysis of treatment effects • How to estimate the effects of the treatment once you have run the RCT? • ITT (ATE) vs TOT (LATE) • HTEs 5. Precision of estimates • How can we improve the precision of the estimation of treatment effects? • ANCOVA 6. Internal and external validity ERB and PAP • Ethics Review Board (ERB) or Institutional Review Board (IRB) • Administrative body established to protect the rights and welfare of human research subjects. • All the project (including questionnaires, potential risks, damages, etc.) • How data and privacy will be protected? Informed consent? Data management protocols? • Is it fair to randomize? • Pre-analysis Plan (PAP) • Register the project before implementing it: Question, method, hypothesis, analysis. • Adds transparency. Separates exploratory from confirmatory analyses • American Economic Association RCT Registry • https://aspredicted.org/ • Berkeley Initiative for Transparency in the Social Sciences (BITSS) RCT Timeline RCT: Data and Timeline Typical Study Listing Baseline Randomization Intervention Midline Endline Long-term effects? • Listing: short census of the population • Baseline survey: typically a 60-90 mins survey (not necessary) • Randomization: assign units (individuals, households, villages, etc) to treatment/control • Midline: short-term (6 months to 12 months) measure of outcome variables (not necessary) • Endline: measure of outcomes after 12 to 24 months • Long-term line: measure of outcomes after 4 or longer years How to randomize? Randomization from scratch: Example ID Gender Age Income Married Wellbeing 1 0 71 112 1 1 Listing exercise 2 1 79 222 1 2 3 0 78 332 0 1 (e.g. 100 people) 4 1 63 32 1 4 5 1 39 44 0 5 6 1 68 656 1 2 7 1 58 77 0 1 Randomly select 8 1 79 878 1 2 Median age 60 9 1 62 932 0 4 Female 1 20 people to the 10 1 36 104 1 5 11 1 32 1155 1 3 study 12 0 54 123 0 2 13 1 52 1323 1 2 14 1 22 124 1 1 15 0 28 1215 0 4 Baseline survey 16 0 79 1361 0 4 17 1 29 173 1 5 18 1 65 148 1 4 19 1 76 192 0 2 20 1 23 204 1 1 Randomization from scratch Above Random Create the variables you ID Gender Age median age number may want to use to stratify 1 0 71 1 0.443881712 the randomization later. 2 1 79 1 0.691642531 (Eg: above median age) 3 0 78 1 0.810024944 4 1 63 1 0.9232475 5 1 39 0 0.689051701 6 1 68 1 0.47461034 Assign a random number 7 1 58 0 0.415243898 to each ID, (e.g. using 8 1 79 1 0.182071105 Rand() in excel, uniform() 9 1 62 1 0.399455999 in STATA, runif(1) in R) 10 1 36 0 0.085861319 11 1 32 0 0.992117141 12 0 54 0 0.85518587 13 1 52 0 0.026121019 Note: If you do it in Excel, 14 1 22 0 0.210388031 “Copy and paste” the random 15 0 28 0 0.303809516 numbers as “values” 16 0 79 1 0.756718513 17 1 29 0 0.322047071 18 1 65 1 0.87360388 19 1 76 1 0.666088779 20 1 23 0 0.081817358 Randomization from scratch Above median Treatment ID Gender Age age Rank Order the data by 13 1 52 0 0.026121019 0 “random number” (e.g. 20 1 23 0 0.081817358 0 ascending order) 10 1 36 0 0.085861319 0 8 1 79 1 0.182071105 0 14 1 22 0 0.210388031 0 15 0 28 0 0.303809516 0 Control 17 1 29 0 0.322047071 0 9 1 62 1 0.399455999 0 Assign the first 10 IDs 7 1 58 0 0.415243898 0 to “Control” (=0) and 1 0 71 1 0.443881712 0 6 1 68 1 0.47461034 1 the second 10 IDs to 19 1 76 1 0.666088779 1 “Treatment” (=1) 5 1 39 0 0.689051701 1 2 1 79 1 0.691642531 1 16 0 79 1 0.756718513 1 3 0 78 1 0.810024944 1 Treatment 12 0 54 0 0.85518587 1 18 1 65 1 0.87360388 1 4 1 63 1 0.9232475 1 11 1 32 0 0.992117141 1 Two types of randomization: Simple or Stratified Simple randomization • Draw a random number from uniform distribution for each observation • Order the random numbers • Set cut-off points (first half control, second half treatment) Do you see any problem with this simple randomization? • If N is small enough, simple randomization can deliver unbalance samples in characteristics that could be correlated with the expected treatment outcome! Let’s check the results of our randomization: Control Treatment p-value Fisher-exact test Female = 1 proportion 80% 70% 1 t-test Age (years) mean 46 63.3 0.05* Do you see any problem? Two types of randomisation: Simple or Stratified Stratified (block) randomization Randomization is performed separately within each stratum.
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