Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/227935 
Erscheinungsjahr: 
2019
Schriftenreihe/Nr.: 
Working Papers No. 19-20
Verlag: 
Federal Reserve Bank of Boston, Boston, MA
Zusammenfassung: 
This paper uses a comprehensive source of yearly data to study private-sector labor demand across US counties during the past five decades. Our focus is on how employment levels and earnings relate to population density - that is, how labor markets in rural areas, suburbs, and cities have fared relative to one another. Three broad lessons emerge. First, the longstanding suburbanization of employment and population in cities with very dense urban cores essentially stopped in the first decade of the 21st century. For cities with less dense cores, however, the decentralization of employment continues, even as population patterns mimic those of denser areas. Second, a dataset that begins in 1964 clearly shows the decentralization of manufacturing employment away from inner cities, a trend that has long been a focus of the urban sociological literature. Starting in the 1990s, however, manufacturing employment fell sharply, not just in cities but also in rural areas, which had experienced less-intense deindustrialization before then. Finally, average earnings dispersion across counties with similar density levels fell during most of our sample period. But after the 1990s, this dispersion rose, probably because of an increase in earnings dispersion among very dense counties ("superstar cities"). We also note that our results are consistent with explanations of rising individual-level earnings inequality within cities that rest on fundamental changes in how basic job tasks are performed, rather than where particular jobs are located.
Schlagwörter: 
demographics
labor economics
economic geography
location economics
realestate economics
regional economics
spatial
JEL: 
J2
J3
J6
R1
R3
Persistent Identifier der Erstveröffentlichung: 
Dokumentart: 
Working Paper

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