Authors: Marynia Kolak*, University of Chicago
Topics: Spatial Analysis & Modeling, Quantitative Methods, Temporal GIS
Keywords: spatial econometrics, counterfactual framework, Great Recession, segregation, food deserts, health disparities
Session Type: Paper
Start / End Time: 5:20 PM / 7:00 PM
Room: Astor Ballroom II, Astor, 2nd Floor
Presentation File: No File Uploaded
The Great Recession impacted the United States with a mélange of increased home foreclosures, increased unemployment, and additional markers of economic decline. This shock likely also impacted neighborhood level markers of community resilience, including access to healthy food. As different processes may drive different trends, isolating unique patterns is essential to testing assumptions that are hypothesized to change the foodscape. A quasi-experimental research design was implemented to distill the effects of the Recession on food access in Chicago, using excess foreclosure risk as a treatment proxy. Because of the strong, consistent spatial patterns made evident by exploratory spatial data analysis (ESDA), a sensitivity analysis of quasi-experimental models using different spatial conceptualizations explored both consistency and variations in treatment. Innovative extensions of a counterfactual framework with panel spatial econometric methods were used in a series of evaluation experiments. Chicago neighborhoods with more foreclosure experienced a small but significant worsening in food accessibility after the Great Recession, even after accounting for variations in income, group effects, and patterns of racial segregation. Persistent trends of inequity across the entire time period study remain, with significantly worse access in segregated black neighborhoods. Inference interpretation is sensitive to both research design framing and underlying processes that drive geographically distributed relationships. For highly spatial phenomenon like segregation and foreclosure, making space explicit may reduce the magnification of certain results.