Evidence for localization and urbanization economies in urban scaling

Non-trivial relationship between scaling behavior and the spatial organization of GDP in Indonesian cities

Urban scaling analysis has shown that various aggregated urban quantities obey power-law relationships with the population size. Despite the rapid progress, direct empirical evidence that shows how the power-law exponents β depend on the spatial organization of the GDP has been lacking. Moreover, urban scaling studies are hardly reproduced in developing countries due to inadequate official statistics. We tackle these issues by performing urban scaling analysis on Indonesian cities using globally harmonized functional cities delineations and global-scale gridded Gross Domestic Product (GDP) datasets. First, we observe that the GDP and area of Indonesian cities scale linearly with the population size. For GDP in particular, the deviations from the scaling law follow a geographical pattern. Second, we determine the economic hotspots in each city and observe that the area of the hotspots scales mildly sublinear with the population size. Surprisingly, the GDP of hotspots also scales sublinearly with the population size, indicating a lack of increasing returns due to scaling. Third, by classifying the cities based on the spatial organization of the GDP in two dimensions (heterogeneity and spatial dispersion) and examining the scaling exponents of each class, we discover a non-trivial relation between scaling behavior and the spatial organization of the GDP. Spatial dispersion strongly affects the scaling behavior in heterogeneous cities, while such effect is weakened for homogeneous cities. Finally, we find that the scaling effect in terms of economies of scale (sublinearity of area) and increasing returns (superlinearity of GDP) is stronger for Indonesian cities with spatially compact GDP distribution.

Rethinking the Contribution of Land Element to Urban Economic Growth: Evidence from 30 Provinces in China

In China, disputes regarding the benefits and drawbacks of land finance have been heated, but the role of land in urban economic growth has received insufficient attention, particularly on a macro scale. This research used the Cobb–Douglas production function model to investigate the role of land in urban economic growth. Then, we conducted an empirical test using panel data from 30 provinces from 2000 to 2019, with the goal of revealing the role of land in urban growth and spatio-temporal inequalities in China. Furthermore, to find the spatial steady-state level of land contributions, σ convergence, absolute β convergence, and condition β convergence tests were applied. The results show that: (1) China’s urban economic development was influenced by the combined element of land, capital, and labor; (2) the contribution of land to China’s urban economy experienced a turning point during the “12th Five-Year Plan”; (3) the spatio-temporal convergence of the contribution of land showed convergence in the east but nonconvergence in the central and western regions; and (4) β convergence demonstrated convergence in eastern, central, and western China. Given the complex and turbulent international political and economic context, the Chinese government should think about how to foster continuous energy by supporting land-supply policies that are adapted to local needs.

Modelling the Relationship of Infrastructure and Externalities Using Urban Scaling

A surprising aspect of the agglomeration economy is the lack of attention to the impact on the physical environment. Even in the field of spatial planning, road infrastructure has been built in situations where the consideration of the agglomeration economy is insufficient. The urban scaling proposed by theoretical physicists is an excellent tool to solve this problem but is only at the level of conceptually comparing the index values extracted by individually scaling socioeconomic indicators and urban infrastructure with the population. Accordingly, the frame model scales the urban infrastructure with the number of workers by industry sector and includes a density externality structure so that the agglomeration economy and urban infrastructure can be linked directly. Three experiments were conducted to verify the frame model: first, the Zipf distribution of economic activity found straight lines in large cities, peaks in medium cities and hills in small cities; the cities were categorised by urban size. The second experiment verified that linearisation was due to Jacobs externalities, while the third confirmed that the peak was due to Marshall–Arrow–Romer externalities. Moreover, in distinguishing traditional and modern industries, thresholds of 0.6 in agglomeration and 1.0 in economic interaction were found.

Estimating the drivers of urban economic complexity and their connection to economic performance

Estimating the capabilities, or inputs of production, that drive and constrain the economic development of urban areas has remained a challenging goal. We posit that capabilities are instantiated in the complexity and sophistication of urban activities, the know-how of individual workers, and the city-wide collective know-how. We derive a model that indicates how the value of these three quantities can be inferred from the probability that an individual in a city is employed in a given urban activity. We illustrate how to estimate empirically these variables using data on employment across industries and metropolitan statistical areas in the USA. We then show how the functional form of the probability function derived from our theory is statistically superior when compared with competing alternative models, and that it explains well-known results in the urban scaling and economic complexity literature. Finally, we show how the quantities are associated with metrics of economic performance, suggesting our theory can provide testable implications for why some cities are more prosperous than others.

On the relation between transversal and longitudinal scaling in cities

Does the scaling relationship between population sizes of cities with urban metrics like economic output and infrastructure (transversal scaling) mirror the evolution of individual cities in time (longitudinal scaling)? The answer to this question has important policy implications, but the lack of suitable data has so far hindered rigorous empirical tests. In this paper, we advance the debate by looking at the evolution of two urban variables, GDP and water network length, for over 5500 cities in Brazil. We find that longitudinal scaling exponents are city-specific. However, they are distributed around an average value that approaches the transversal scaling exponent provided that the data is decomposed to eliminate external factors, and only for cities with a sufficiently high growth rate. We also introduce a mathematical framework that connects the microscopic level to global behaviour, finding good agreement between theoretical predictions and empirical evidence in all analyzed cases. Our results add complexity to the idea that the longitudinal dynamics is a micro-scaling version of the transversal dynamics of the entire urban system. The longitudinal analysis can reveal differences in scaling behavior related to population size and nature of urban variables. Our approach also makes room for the role of external factors such as public policies and development, and opens up new possibilities in the research of the effects of scaling and contextual factors.