@article {141,
	title = {Alleviating Linear Ecological Bias and Optimal Design with Subsample Data},
	journal = {Journal of the Royal Statistical Society: Series A},
	volume = {171},
	number = {1},
	year = {2008},
	pages = {179-202},
	abstract = {In this paper, we illustrate that combining ecological data with subsample data in situations in which a linear model is appropriate provides two main benefits. First, by including the individual level subsample data, the biases associated with linear ecological inference can be eliminated. Second, we can use readily available ecological data to design optimal subsampling schemes, so as to maximize information about parameters. We present an application of this methodology to the classic problem of estimating the effect of a college degree on wages, showing that small, optimally chosen subsamples can be combined with ecological data to generate precise estimates relative to a simple random subsample.},
	author = {Adam Glynn and Jon Wakefield and Mark Handcock and Thomas Richardson}
}
