Classifying Opportunity Zones- A Model-Based Clustering Approach

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Departmental Papers (City and Regional Planning)
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Planning
Public Development
Urban, Community and Regional Planning
Urban Studies and Planning
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Objective: Opportunity Zones (OZs) are the first major place-based economic development policy from the federal government in nearly two decades. To date, confusion persists among planners and policymakers in some places as to what features of OZ tracts matter for their inclusion, and, secondly, what features of OZ tracts make them attractive targets for potential investment. The authors developed a typology of OZ tracts in order to offer planners and policymakers alternative ways of organizing a highly variable set of tracts. Methods: This study employs model-based clustering, also known as latent class analysis, to develop a typology OZ tracts from the population of all eligible tracts in the United States. The authors use publicly available data from the US Census Bureau and Urban Institute in developing the typology. Descriptive statistics and graphics are presented on the clusters. Using Portland, Oregon as an example city, the authors present a cartographic exploration of the resulting typology. Results: OZs present with immense variation across clusters. Some clusters, specifically cluster 3 and 9, are less poor, have a greater number of jobs and higher development potential than other clusters. Additionally, these exceptional clusters have disproportionate rates of final OZ designation compared to other clusters. In Portland, these less distressed clusters make up the majority of ultimately designated OZ tracts in the city and are concentrated in the downtown area compared to the more deprived eastern part of the city. Conclusions: We find that OZ designation is disproportionately seen in particular clusters that are relatively less deprived than the larger population of eligible tracts. Cluster analysis as well as other forms of exploratory or inductive analyses can offer planners and policymakers a better understanding of their local development context as well as offering a more coherent understanding of a widely variant set of tracts.

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2022-01-01
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