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What We Can Learn from Small Units of Analysis

38 Pages Posted: 4 Jul 2015  

Andrew Palmer Wheeler

University of Texas at Dallas - School of Economic, Political and Policy Sciences

Date Written: July 3, 2015

Abstract

This article provides motivation for examining small geographic units of analysis based on a causal logic framework. Local, spatial, and contextual effects are confounded when using larger units of analysis, as well as treatment effect heterogeneity. I relate these types of confounds to all types of aggregation problems, including temporal aggregation, and aggregation of dependent or explanatory variables. Unlike prior literature critiquing the use of aggregate level data, examples are provided where aggregation is unlikely to hinder the goals of the particular research design, and how heterogeneity of measures in smaller units of analysis is not a sufficient motivation to examine small geographic units. Examples of these confounds are presented using simulation with a dataset of crime at micro place street units (i.e. street segments and intersections) in Washington, D.C.

Keywords: ecological-fallacy, aggregation, causal inference, micro-units

Suggested Citation

Wheeler, Andrew Palmer, What We Can Learn from Small Units of Analysis (July 3, 2015). Available at SSRN: https://ssrn.com/abstract=2626564 or http://dx.doi.org/10.2139/ssrn.2626564

Andrew Palmer Wheeler (Contact Author)

University of Texas at Dallas - School of Economic, Political and Policy Sciences ( email )

P.O. Box 830688, GR 31
Richardson, TX 75083
United States

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