# Ecological Regression with Partial Identification

Wenxin Jiang, Gary King (), Allen Schmaltz and Martin A. Tanner

Political Analysis, 2020, vol. 28, issue 1, 65-86

Abstract: 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.

Date: 2020
Citations: Track citations by RSS feed

https://www.cambridge.org/core/product/identifier/ ... type/journal_article link to article abstract page (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text