Bridging the gap between geographic concept and the data we have

The case of labor markets in the USA

Bibliographic Data

ID4983844
AuthorsC S Fowler (0000-0001-8415-0441, Department of Geography and the Population Research Institute, The Pennsylvania State University, USA, corresponding author), Leif Jensen (0000-0002-3842-3004, Department of Geography and the Population Research Institute, The Pennsylvania State University, USA)
Year2020
Volume52
Issue7
Pages1395-1414
Publication date2020-10-01
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueEnvironment and Planning A Economy and Space (JOURNAL)
Journal identifiersISSN: 0308-518X • E-ISSN: 1472-3409
PublisherSAGE Publications Inc (PUBLISHER)
DOI10.1177/0308518x20906154
OpenAlexW3006310347
LanguageEN
Citations received22
References cited41

A broad literature has made it clear that geographic units must be selected with care or they are likely to introduce error and uncertainty into results. Nevertheless, researchers often use data "off the shelf" with the implicit assumptions that their observations are consistent with the geographical concept relevant for their research question, and that they are of uniformly high quality in capturing this geographic identity. In this paper, we consider the geographical concept of "labor market" and offer a template for both clarifying its meaning for research and testing the suitability of extant labor-market delineations. We establish a set of metrics for comparing the quality of existing labor-market delineations with respect to the diverse meanings that researchers apply to the concept. Using the fit metrics established here, researchers can explore how delineations vary geographically, how they vary over time, and how this variation may shape research outcomes. Our assessment is that the quality of the extant delineations is relatively high overall. However, we find that different delineations vary significantly in the types of labor markets they represent, and that regional variations in fit within any given delineation may introduce noise or regional bias that merits consideration in any analysis conducted with these units. More broadly, the kinds of metrics we propose here have applicability for many other geographic entities where boundaries and scale can be only imperfectly defined

Data science · Econometrics · Economics · Epistemology · Extant taxon · Computer Science · Regional Economics and Spatial Analysis · Spatial and Panel Data Analysis · Urban, Neighborhood, and Segregation Studies

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