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Ecological Regression with Partial Identification

Published online by Cambridge University Press:  02 August 2019

Wenxin Jiang ,
Gary King Open the ORCID record for Gary King [Opens in a new window] ,
Allen Schmaltz Open the ORCID record for Allen Schmaltz [Opens in a new window]  and
Martin A. Tanner
Wenxin Jiang
Affiliation:
Institute of Finance (Adjunct), Shandong University, Jinan, Shandong, China. Email: [email protected] Department of Statistics, Northwestern University, Evanston, IL, USA. Email: [email protected]
Gary King
Affiliation:
Institute for Quantitative Social Science, Harvard University, Cambridge, MA, USA. Email: [email protected], [email protected]
Allen Schmaltz*
Affiliation:
Institute for Quantitative Social Science, Harvard University, Cambridge, MA, USA. Email: [email protected], [email protected]
Martin A. Tanner
Affiliation:
Department of Statistics, Northwestern University, Evanston, IL, USA. Email: [email protected]
*
*Email: [email protected]
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Abstract

Ecological inference (EI) is the process of learning about individual behavior from aggregate data. We relax assumptions by allowing for “linear contextual effects,” which previous works have regarded as plausible but avoided due to nonidentification, a problem we sidestep by deriving bounds instead of point estimates. In this way, we offer a conceptual framework to improve on the Duncan–Davis bound, derived more than 65 years ago. To study the effectiveness of our approach, we collect and analyze 8,430 $2\times 2$ EI datasets with known ground truth from several sources—thus bringing considerably more data to bear on the problem than the existing dozen or so datasets available in the literature for evaluating EI estimators. For the 88% of real data sets in our collection that fit a proposed rule, our approach reduces the width of the Duncan–Davis bound, on average, by about 44%, while still capturing the true district-level parameter about 99% of the time. The remaining 12% revert to the Duncan–Davis bound.

Keywords

asymptoticsboundsconfidence intervalscontextual modelsecological inferencelinear regressionpartial identification
Type
Articles
Information
Political Analysis , Volume 28 , Issue 1 , January 2020 , pp. 65 - 86
Copyright
Copyright © The Author(s) 2019. Published by Cambridge University Press on behalf of the Society for Political Methodology. 

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Footnotes

Authors’ note: We thank the editor and anonymous reviewers for their helpful comments. This work was partially supported by the Taishan Scholar Construction Project to W.J. and by the Institute for Quantitative Social Science.

Contributing Editor: Jeff Gill

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