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Scaling trajectories of cities
Urban scaling research finds that agglomeration effects—the higher-than-expected outputs of larger cities—follow robust “superlinear” scaling relations in cross-sectional data. But the paradigm has predictive ambitions involving the dynamic scaling of individual cities over many time points and expe...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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National Academy of Sciences
2019
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6628653/ https://www.ncbi.nlm.nih.gov/pubmed/31235579 http://dx.doi.org/10.1073/pnas.1906258116 |
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author | Keuschnigg, Marc |
author_facet | Keuschnigg, Marc |
author_sort | Keuschnigg, Marc |
collection | PubMed |
description | Urban scaling research finds that agglomeration effects—the higher-than-expected outputs of larger cities—follow robust “superlinear” scaling relations in cross-sectional data. But the paradigm has predictive ambitions involving the dynamic scaling of individual cities over many time points and expects parallel superlinear growth trajectories as cities’ populations grow. This prediction has not yet been rigorously tested. I use geocoded microdata to approximate the city-size effect on per capita wage in 73 Swedish labor market areas for 1990–2012. The data support a superlinear scaling regime for all Swedish agglomerations. Echoing the rich-get-richer process on the system level, however, trajectories of superlinear growth are highly robust only for cities assuming dominant positions in the urban hierarchy. |
format | Online Article Text |
id | pubmed-6628653 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | National Academy of Sciences |
record_format | MEDLINE/PubMed |
spelling | pubmed-66286532019-07-22 Scaling trajectories of cities Keuschnigg, Marc Proc Natl Acad Sci U S A Social Sciences Urban scaling research finds that agglomeration effects—the higher-than-expected outputs of larger cities—follow robust “superlinear” scaling relations in cross-sectional data. But the paradigm has predictive ambitions involving the dynamic scaling of individual cities over many time points and expects parallel superlinear growth trajectories as cities’ populations grow. This prediction has not yet been rigorously tested. I use geocoded microdata to approximate the city-size effect on per capita wage in 73 Swedish labor market areas for 1990–2012. The data support a superlinear scaling regime for all Swedish agglomerations. Echoing the rich-get-richer process on the system level, however, trajectories of superlinear growth are highly robust only for cities assuming dominant positions in the urban hierarchy. National Academy of Sciences 2019-07-09 2019-06-24 /pmc/articles/PMC6628653/ /pubmed/31235579 http://dx.doi.org/10.1073/pnas.1906258116 Text en Copyright © 2019 the Author(s). Published by PNAS. http://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by/4.0/This open access article is distributed under Creative Commons Attribution License 4.0 (CC BY) (http://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Social Sciences Keuschnigg, Marc Scaling trajectories of cities |
title | Scaling trajectories of cities |
title_full | Scaling trajectories of cities |
title_fullStr | Scaling trajectories of cities |
title_full_unstemmed | Scaling trajectories of cities |
title_short | Scaling trajectories of cities |
title_sort | scaling trajectories of cities |
topic | Social Sciences |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6628653/ https://www.ncbi.nlm.nih.gov/pubmed/31235579 http://dx.doi.org/10.1073/pnas.1906258116 |
work_keys_str_mv | AT keuschniggmarc scalingtrajectoriesofcities |