Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/142347
Authors: 
Carneiro, Pedro
Lee, Sokbae
Wilhelm, Daniel
Year of Publication: 
2016
Series/Report no.: 
IZA Discussion Papers 9908
Abstract: 
In a randomized control trial, the precision of an average treatment effect estimator can be improved either by collecting data on additional individuals, or by collecting additional covariates that predict the outcome variable. We propose the use of pre-experimental data such as a census, or a household survey, to inform the choice of both the sample size and the covariates to be collected. Our procedure seeks to minimize the resulting average treatment effect estimator's mean squared error, subject to the researcher's budget constraint. We rely on a modification of an orthogonal greedy algorithm that is conceptually simple and easy to implement in the presence of a large number of potential covariates, and does not require any tuning parameters. In two empirical applications, we show that our procedure can lead to substantial gains of up to 58%, measured either in terms of reductions in data collection costs or in terms of improvements in the precision of the treatment effect estimator.
Subjects: 
randomized control trials
big data
data collection
optimal survey design
orthogonal greedy algorithm
survey costs
JEL: 
C55
C81
Document Type: 
Working Paper

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