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L. Bettencourt, H. Samaniego, Hyejin Youn (2012)
Professional diversity and the productivity of citiesScientific Reports, 4
D. Pumain, Fabien Paulus, Céline Vacchiani-Marcuzzo, J. Lobo (2006)
An evolutionary theory for interpreting urban scaling laws Une théorie évolutive pour expliquer les lois d'échelle dans les systèmes de villes
F. Mameli, A. Faggian, P. McCann (2008)
Employment Growth in Italian Local Labour Systems: Issues of Model Specification and Sectoral AggregationSpatial Economic Analysis, 3
Nathaniel Baum-Snow, Ronni Pavan (2013)
Inequality and City SizeReview of Economics and Statistics, 95
(2011)
Spatial inequality of Australian men's incomes: 1991 to
L. Bettencourt, J. Lobo, D. Helbing, C. Kühnert, G. West (2007)
Growth, innovation, scaling, and the pace of life in citiesProceedings of the National Academy of Sciences, 104
E. Glaeser, Matthew Resseger (2009)
The Complementarity between Cities and SkillsMicroeconomics: Production
M. Turshen (2001)
Development as FreedomJournal of Public Health Policy, 22
R. Paddison (2012)
Triumph of the CityHousing Studies, 27
M. Batty (2013)
The New Science of Cities
H. Groot, G. Marlet, C. Teulings, W. Vermeulen (2015)
The consumer city
Tomoya Mori, Koji Nishikimi, T. Smith (2008)
The Number Average Size Rule: A New Empirical Relationship between Industrial Location and City SizeUrban Economics & Regional Studies (Forthcoming)
(2016)
Defining urban agglomerations to detect agglomeration economies
(2016)
Regional Migration Estimates 2014-15
D. Pumain (2012)
Urban Systems Dynamics, Urban Growth and Scaling Laws: The Question of Ergodicity
Somwrita Sarkar, P. Phibbs, Roderick Simpson, Sachin Wasnik (2018)
The scaling of income distribution in Australia: Possible relationships between urban allometry, city size, and economic inequalityEnvironment and Planning B: Urban Analytics and City Science, 45
Jorge Leitao, J. Miotto, Martin Gerlach, E. Altmann (2016)
Is this scaling nonlinear?Royal Society Open Science, 3
Rémi Louf, M. Barthelemy (2014)
Scaling: Lost in the SmogEnvironment and Planning B: Planning and Design, 41
Masahisa Fujita, P. Krugman, A. Venables (1999)
The Spatial Economy: Cities, Regions, and International Trade, 1
(2014)
Commentary, scaling: Lost in the smog. Environment and Planning B
D. Pumain (2004)
Scaling laws and urban systems
M. Beckmann (1958)
City Hierarchies and the Distribution of City SizeEconomic Development and Cultural Change, 6
(1989)
Urban Economics: Land Use and City Size
C. Shalizi (2011)
Scaling and Hierarchy in Urban EconomiesarXiv: Applications
A. Dong, Somwrita Sarkar, C. Nichols, T. Kvan (2013)
The capability approach as a framework for the assessment of policies toward civic engagement in designDesign Studies, 34
K. Behrens, Frédéric Robert-Nicoud (2014)
Survival of the Fittest in Cities: Urbanisation and InequalityLabor: Supply & Demand eJournal
F. E.
Principles of EconomicsNature, 42
J. Portugali (2011)
Complexity Theories of Cities Have Come of Age: Achievements, Criticism, and Potentials
D. Pumain (2006)
Alternative Explanations of Hierarchical Differentiation in Urban Systems, 3
L. Pil (2004)
The Rise of the Creative Class
E. Arcaute, E. Hatna, P. Ferguson, Hyejin Youn, Anders Johansson, M. Batty (2013)
Constructing cities, deconstructing scaling lawsJournal of the Royal Society Interface, 12
Urban scaling laws summarise how socio-economic behaviours of urban systems may be predicted from city size. While most scaling analysis rests on using aggregate quantities (total incomes, GDP, etc.), examining distributions of these aggregate quantities (e.g. income distributions) could shed light on how socio-economic inequalities may correlate or be causally linked to city size. In this direction, this paper examines how geographic distributions and spatial inequalities of income and housing costs vary by city size. The paper presents three principal results. First, it brings out qualitative implications of quantitative scaling by relating scaling of the distributions of income and housing costs to their specific geographic concentrations. Second, it shows that some small and medium sized cities are clear outliers, showing behaviour similar to the largest cities and starkly different from the behaviours of the bulk of small and medium sized cities. Third, this above observation explains why heteroscedasticity, or large and heterogeneous fluctuations, are frequently observed in urban indicator data when plotted as a function of city size. Putting together these three results, overall, it is shown that income distributions and housing costs scale and concentrate in cities by size in a predictable way, where the largest cities superlinearly/disproportionately agglomerate the highest income earners and the highest housing costs, and show relatively lower concentrations of low-middle income earners and low-medium housing costs. In contrast, most of the smaller and medium sized cities show a ‘flipped’ opposite trend. A few small and medium sized cities are outliers: they show trends that match those of the largest cities, due to specialisations of economic functions or concentrations of high-paying occupations in these cities. The empirical findings lead to a discussion on the objective and normative relationships between city size and urban inequalities. It is suggested that due to the concentrations of high income and high housing costs, largest cities may have a resulting housing market structure that will push out lower and medium income earners, thereby making affordability, diversity, and socio-spatial justice emerge as important urban policy issues.
Environment and Planning B – SAGE
Published: Nov 1, 2019
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