Ecological Regression with Partial Identification

Citation:

Wenxin Jiang, Gary King, Allen Schmaltz, and Martin A. Tanner. Forthcoming. “Ecological Regression with Partial Identification.” Political Analysis. Copy at http://j.mp/2vh3O93
Paper385 KB
Online Supplementary Appendix186 KB
Ecological Regression with Partial Identification

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 non-identification, 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 sixty-five years ago. To study the effectiveness of our approach, we collect and analyze 8,430 datasets with known ground truth -- 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

 

Last updated on 01/28/2019