Urbanization favors high wage earners

Shutters, Shade T.; Applegate, J. M.; Wentz, Elizabeth A.; Batty, Michael

doi:10.1038/s42949-022-00049-x

Cited by 14 publications

(7 citation statements)

References 38 publications

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“…Recent research has taken important steps along the lines argued for here. First, empirical methodology has been developed to capture within-city variation in scaling [23][24][25] and to ensure the robust estimation of scaling exponents in the light of heavy-tailed urban indicators 57 . Second, a recently proposed theoretical framework 17 has acknowledged heterogeneity and inequality in urbanization processes.…”

Section: Discussionmentioning

confidence: 99%

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Urban scaling laws arise from within-city inequalities

2023

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Theories of urban scaling have demonstrated remarkable predictive accuracy at aggregate levels. However, they have overlooked the stark inequalities that exist within cities. Human networking and productivity exhibit heavy-tailed distributions, with some individuals contributing disproportionately to city totals. Here we use micro-level data from Europe and the United States on interconnectivity, productivity and innovation in cities. We find that the tails of within-city distributions and their growth by city size account for 36–80% of previously reported scaling effects, and 56–87% of the variance in scaling between indicators of varying economic complexity. Providing explanatory depth to these findings, we identify a mechanism—city size-dependent cumulative advantage—that constitutes an important channel through which differences in the size of tails emerge. Our findings demonstrate that urban scaling is in large part a story about inequality in cities, implying that the causal processes underlying the heavier tails in larger cities must be considered in explanations of urban scaling. This result also shows that agglomeration effects benefit urban elites the most, with the majority of city dwellers partially excluded from the socio-economic benefits of growing cities.

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Section: Discussionmentioning

confidence: 99%

“…Power laws are common in nature and society, present not only as scaling laws between cities but also as extremely skewed distributions within agglomerations 22 . Consequently, sums and means are poor and potentially misleading indicators of the relevant quantities of cities [23][24][25] .…”

Section: The Role Of City Tails For Superlinear Scalingmentioning

confidence: 99%

Urban scaling laws arise from within-city inequalities

2023

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“…bigger the city, the more unjust it is to the poor (Mora et al, 2021;Sarkar et al, 2018;Shutters et al, 2022). But, so far, we have no theory or analytics to propose any urban welfare function(s) that can counter the inequality, injustice, and unsustainability outcomes that emerge from such natural power law distributions of city sizes.…”

Section: Declaration Of Conflicting Interestsmentioning

confidence: 99%

Normative urban science

Sarkar

2024

Environment and Planning B: Urban Analytics and City Science

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Our task in City Science is to create theories and models for what cities are, how they come to be, and the mechanisms and processes of their formation, maintenance, and decline. Urban Analytics then employs empirical data-driven methods to describe and analyse what is, and evaluate, test, and calibrate the theories and models. Together, they enable us to understand the physics, socioeconomics, and technologies of cities: their structure and form, of space and in space, the dynamics of flows and interactions within this form and space, and the entangled, interactional, morphological complexity of form/structure, behaviour, and function. But ultimately, cities are human agglomerations. The buildings, roads, urban spaces, and places exist to enable human interactions, whether these be social, economic, technological, or some combinations of these. Cities are the physical home in which the historical, economic, and social is lived, achieved, and realised. The city is the infrastructural basis which enables social, economic, and technological interactions to be actuated.So far, our science has made tremendous advances to the point where it is able to describe what a city is, how it came to be. And it can do this to an unprecedented level of spatial and temporal detail, a task that would seem Herculean and impossible only a couple of decades ago (Batty, 2024). Nonetheless, the future of our science will rest on how well our science is able to tell us what a city should be. It is time for a phase shift to occur in our science, towards building a Normative Urban Science, which should be informed by but distinguished from a Positive Urban Science.So far, our modern urban science tends much more towards the positive of what is, and what can be, rather than the normative of what ought to be. But perhaps it is too simplistic to generalise this view? After all, one could argue that the modern discipline of urban planning owes its very existence and development to the normative concern of addressing poor conditions of health and hygiene in cities during the industrial revolution. At its very root, the act of doing architecture and planning is a normative actthe planner, designer, or architect is primarily interested in shaping the future of spaces, places, networks, and forms. So, the act of building cities is inherently an imaginative and inventive act, an act of design (Batty, 2024), whether actuated centrally through a single agent or in a more distributed way through millions of agents. The question is: does the act always derive from a normative basis of reasoning?

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“…Income inequality presents a key challenge to urban sustainability in the developed economies (Cantante, 2020 ; Chancel et al, 2018 ). Cities having big income divide may also be characterised by unequal access to higher education, employment, or safety protection opportunities (Schneider, 2016 ; Shutters et al, 2022 ; Wan et al, 2022 ). To address such inequality, accurate representation of income distribution in high granularity and its determinants are crucial for evidence-based policy-making (Suel et al, 2018 ).…”

Section: Introductionmentioning

confidence: 99%

What dictates income in New York City? SHAP analysis of income estimation based on Socio-economic and Spatial Information Gaussian Processes (SSIG)

Bai

Lam

2023

Humanit Soc Sci Commun

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Income inequality presents a key challenge to urban sustainability across the developed economies. Traditionally, accurate high granularity income data are generally obtained from field surveys. However, due to privacy considerations, field subjects are hesitant to provide accurate personal income data. A Socio-economic & Spatial-Information-GP (SSIG) model is thereby developed to estimate district-based high granularity income for New York City (NYC). As compared to the state-of-the-art Gaussian Processes (GP) income estimation model based entirely on spatial information, SSIG incorporates socio-economic domain-specific knowledge into a GP model. For SSIG to be explainable, SHapley Additive exPlanations (SHAP) analysis is undertaken to evaluate the relative contribution of various key individual socio-economic variables to district-based per-capita and median household income in NYC. Differentiating from traditional income inequality studies based predominantly on linear or log-linear regression model, SSIG presents a novel income-based model architecture, capable of modelling complex non-linear relationships. In parallel, SHAP analysis serves an effective analytical tool for identifying the key attributes to income inequality. Results have shown that SSIG surpasses other state-of-the-art baselines in estimation accuracy, as far as per-capita and median household income estimation at the Tract-level and the ZIP-level in NYC are concerned. SHAP results have indicated that having a bachelor or a postgraduate degree can accurately predict income in NYC, despite that between-district income inequality due to Sex/Race remains prevalent. SHAP has further confirmed that between-district income gap is more associated with Race than Sex. Furthermore, ablation study shows that socio-economic information is more predictive of income at the ZIP-level, relative to the spatial information. This study carries significant implications for policy-making in a developed context. To promote urban economic sustainability in NYC, policymakers can attend to the growing income disparity (income inequality) contributed by Sex and Race, while giving more higher education opportunities to residents in the lower-income districts, as the estimated per-capita income is more sensitive to the proportion of adults ≥25 holding a bachelor’s degree. Finally, interpretative SHAP analysis is useful for investigating the relative contribution of socio-economic inputs to any predicted outputs in future machine-learning-driven socio-economic analyses.

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Urbanization favors high wage earners

Cited by 14 publications

References 38 publications

Urban scaling laws arise from within-city inequalities

Urban scaling laws arise from within-city inequalities

Normative urban science

What dictates income in New York City? SHAP analysis of income estimation based on Socio-economic and Spatial Information Gaussian Processes (SSIG)

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