Ecological Regression with Partial Identification
Gary King (),
Allen Schmaltz and
Martin A. Tanner
Political Analysis, 2020, vol. 28, issue 1, 65-86
Ecological inference (EI) is the process of learning about individual behavior from aggregate data. We relax assumptions by allowing for â€œlinear contextual effects,â€ 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 Duncanâ€“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 sourcesâ€”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 Duncanâ€“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 Duncanâ€“Davis bound.
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Persistent link: https://EconPapers.repec.org/RePEc:cup:polals:v:28:y:2020:i:1:p:65-86_4
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