Many factors have prompted the adoption of partial-market exit strategies in retail as a means of reducing cost and minimizing risk. These mass closures have become more frequent in recent years. Marketers and economists have offered explanations for these closures linked to the rise of e-commerce, the real estate cycle and general changes in consumer taste. The research here marks an attempt to apply geospatial and econometric methods to better understand what factors explain the spatial variation of these closures across the United States. Specifically, the analysis examines the store networks of Sears, J.C. Penney and Macy's- large, established department stores that, collectively, announced over 100 closures at the beginning of 2017. By treating each store as a unit of observation, and a closure as a limited dependent variable, this analysis will attempt to quantify the relationship between place-specific factors and retail closures using Probit modeling. This application of modeling marks a deviation from traditional analyses in retail geography which, up until the early 2000s, have focused almost entirely on store development and growth. The results reveal patterns of spatial clustering of closures in and around the Rust Belt and demonstrate the strong negative effect of competitive agglomeration on the probability of closure.
| Identifer | oai:union.ndltd.org:unt.edu/info:ark/67531/metadc1505160 |
| Date | 05 1900 |
| Creators | Reed, Connor |
| Contributors | Rice, Murray D., Leonard, Tammy, Tiwari, Chetan |
| Publisher | University of North Texas |
| Source Sets | University of North Texas |
| Language | English |
| Detected Language | English |
| Type | Thesis or Dissertation |
| Format | vii, 59 pages, Text |
| Rights | Public, Reed, Connor, Copyright, Copyright is held by the author, unless otherwise noted. All rights Reserved. |
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